wbjeon2k

Awesome List of Continual Learning

Awesome Continual Learning

Github의 xialeiliu/Awesome-Incremental-Learning 의 reading list를 퍼왔다.

AI 분야에서 1년간 발표되는 모든 논문을 전부 다 읽는것은 불가능하다.
하지만 많이 읽을수록 연구 동향을 파악하고 주제 선정을 하는데 도움이 되는것은 의심의 여지가 없다.

신년에는 다들 지킬 수 없는 목표를 하나씩 세우는 것 처럼,
이번 신년에는 3월 개강 전 까지 아래에 소개된 모든 논문들을 읽어보는 것을 목표로 정했다!

읽은 것은 블로그 포스트로 정리하면서, 하나씩 취소선으로 지워나가며 꾸준히 업데이트를 할 예정이다.
이미 읽어 봤거나 들어본 논문보다 처음 보는 논문이 많은 것에 그 동안 논문 읽기가 소홀했음을 알게되었다.

disclaimer : 다른 분야에서 두각을 나타내는 박재우 군이 자기 분야에서 출간 된 한 해 논문을 전부 읽었다는 얘기에 영감을 받았다.

정리하는 양식은 다음과 같다.

## Name of the paper
### Quick Look
- **Authors & Affiliation**: [Authors][Affiliations]
- **Link**: [Paper link]
- **Comments**: [e.g. Published at X / arXiv paper / in review.]
- **TLDR**: [One or at most two line summary]
- **Relevance**: [Score between 1 and 5, stating how relevant this paper is to your work. Usually filled in at the end.]
### Research Topic
- **Category** (General):
- **Category** (Specific):
### Paper Summary (What)
[Summary of the paper - a few sentences with bullet points. What did they do?]

2022

  • FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning (WACV2023)[paper]
  • Balanced softmax cross-entropy for incremental learning with and without memory (CVIU)[paper]
  • Online Continual Learning through Mutual Information Maximization (ICML2022)[paper]
  • Improving Task-free Continual Learning by Distributionally Robust Memory Evolution (ICML2022)[paper]
  • Forget-free Continual Learning with Winning Subnetworks (ICML2022)[paper] review
  • NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks (ICML2022)[paper]
  • Continual Learning via Sequential Function-Space Variational Inference (ICML2022)[paper]
  • On Solving Class Incremental Learning in Continual Learning (NeurIPS2022)
  • A Theoretical Study on Solving Continual Learning (NeurIPS2022) [paper] [code]
  • Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer (NeurIPS2022)
  • Memory Efficient Continual Learning with Transformers (NeurIPS2022) [paper]
  • Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation (NeurIPS2022) [paper] [code]
  • Disentangling Transfer in Continual Reinforcement Learning (NeurIPS2022) [paper]
  • Task-Free Continual Learning via Online Discrepancy Distance Learning (NeurIPS2022) [paper]
  • A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal (NeurIPS2022) [paper]
  • S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning (NeurIPS2022) [paper]
  • Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting (NeurIPS2022) [paper]
  • Few-Shot Continual Active Learning by a Robot (NeurIPS2022) [paper]
  • Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions(NeurIPS2022) [paper]
  • SparCL: Sparse Continual Learning on the Edge(NeurIPS2022) [paper]my_review
  • CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks (NeurIPS2022) [paper] [code]
  • Continual Learning In Environments With Polynomial Mixing Times (NeurIPS2022) [paper] [code]
  • Exploring Example Influence in Continual Learning (NeurIPS2022) [paper] [code]
  • ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation (NeurIPS2022) [paper]
  • On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning (NeurIPS2022) [paper] [code]
  • On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting (NeurIPS2022)[paper]
  • CGLB: Benchmark Tasks for Continual Graph Learning (NeurIPS2022)[paper] [code]
  • How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? (NeurIPS2022)[paper]
  • CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One (ECCV2022)[paper] [code]
  • Generative Negative Text Replay for Continual Vision-Language Pretraining (ECCV2022) [paper]
  • DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning (ECCV2022) [paper] [code]
  • The Challenges of Continuous Self-Supervised Learning (ECCV2022)[paper]
  • Helpful or Harmful: Inter-Task Association in Continual Learning (ECCV2022)[paper]
  • incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection (ECCV2022)[paper]
  • S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning (ECCV2022)[paper]
  • Online Task-free Continual Learning with Dynamic Sparse Distributed Memory (ECCV2022)[paper][code]
  • Balancing between Forgetting and Acquisition in Incremental Subpopulation Learning (ECCV2022)[paper]
  • Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer (ECCV2022) [paper] [code]
  • FOSTER: Feature Boosting and Compression for Class-Incremental Learning (ECCV2022) [paper] [code]
  • Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (ECCV2022) [paper]
  • R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning (ECCV2022) [paper] [code]
  • DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning (ECCV2022) [paper]
  • Learning with Recoverable Forgetting (ECCV2022) [paper]
  • Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation (ECCV2022) [paper] [code]
  • Balancing Stability and Plasticity through Advanced Null Space in Continual Learning (ECCV2022) [paper]
  • Long-Tailed Class Incremental Learning (ECCV2022) [paper]
  • Anti-Retroactive Interference for Lifelong Learning (ECCV2022) [paper]
  • Novel Class Discovery without Forgetting (ECCV2022) [paper]
  • Class-incremental Novel Class Discovery (ECCV2022) [paper]
  • Few-Shot Class Incremental Learning From an Open-Set Perspective(ECCV2022)[paper]
  • Incremental Task Learning with Incremental Rank Updates(ECCV2022)[paper]
  • Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay(ECCV2022)[paper]
  • Online Continual Learning with Contrastive Vision Transformer (ECCV2022)[paper]

  • Transfer without Forgetting (ECCV2022) [paper][code]

  • Continual Training of Language Models for Few-Shot Learning (EMNLP2022) [paper] [code]
  • Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation (TPAMI2022) [paper]
  • MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning (TPAMI2022) [paper]
  • Class-Incremental Continual Learning into the eXtended DER-verse (TPAMI2022) [paper] [code]
  • Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks (TPAMI2022) [paper] [code]
  • Continual Semi-Supervised Learning through Contrastive Interpolation Consistency (PRL2022) [paper][code]
  • GCR: Gradient Coreset Based Replay Buffer Selection for Continual Learning (CVPR2022) [paper]
  • Learning Bayesian Sparse Networks With Full Experience Replay for Continual Learning (CVPR2022) [paper]
  • Continual Learning With Lifelong Vision Transformer (CVPR2022) [paper]
  • Towards Better Plasticity-Stability Trade-Off in Incremental Learning: A Simple Linear Connector (CVPR2022) [paper]
  • Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (CVPR2022) [paper]
  • Continual Learning for Visual Search with Backward Consistent Feature Embedding (CVPR2022) [paper]
  • Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries (CVPR2022) [paper]
  • Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency (CVPR2022) [paper]
  • Bring Evanescent Representations to Life in Lifelong Class Incremental Learning (CVPR2022) [paper]
  • Lifelong Graph Learning (CVPR2022) [paper]
  • Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation (CVPR2022) [paper]
  • vCLIMB: A Novel Video Class Incremental Learning Benchmark (CVPR2022) [paper]
  • Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation(CVPR2022) [paper]
  • Few-Shot Incremental Learning for Label-to-Image Translation (CVPR2022) [paper]
  • MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning (CVPR2022) [paper]
  • Incremental Learning in Semantic Segmentation from Image Labels (CVPR2022) [paper]
  • Self-Supervised Models are Continual Learners (CVPR2022) [paper] [code]
  • Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data (CVPR2022) [paper]
  • General Incremental Learning with Domain-aware Categorical Representations (CVPR2022) [paper]
  • Constrained Few-shot Class-incremental Learning (CVPR2022) [paper]
  • Overcoming Catastrophic Forgetting in Incremental Object Detectionvia Elastic Response Distillation (CVPR2022) [paper]
  • Class-Incremental Learning with Strong Pre-trained Models (CVPR2022) [paper]
  • Energy-based Latent Aligner for Incremental Learning (CVPR2022) [paper] [code]
  • Meta-attention for ViT-backed Continual Learning (CVPR2022) [paper] [code]
  • Learning to Prompt for Continual Learning (CVPR2022) [paper] [code]
  • On Generalizing Beyond Domains in Cross-Domain Continual Learning (CVPR2022) [paper]
  • Probing Representation Forgetting in Supervised and Unsupervised Continual Learning (CVPR2022) [paper]
  • Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding (CVPR2022) [paper] [code]
  • Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning (CVPR2022) [paper] [code]
  • Forward Compatible Few-Shot Class-Incremental Learning (CVPR2022) [paper] [code]
  • Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning (CVPR2022) [paper]
  • DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion (CVPR2022) [paper]
  • Federated Class-Incremental Learning (CVPR2022) [paper] [code]
  • Representation Compensation Networks for Continual Semantic Segmentation (CVPR2022) [paper]
  • A Multi-Head Model for Continual Learning via Out-of-Distribution Replay (CoLLAs2022) [paper] [code]
  • Continual Attentive Fusion for Incremental Learning in Semantic Segmentation (TMM2022) [paper]
  • Self-training for class-incremental semantic segmentation (TNNLS2022) [paper]
  • Effects of Auxiliary Knowledge on Continual Learning (ICPR2022) [paper]
  • Continual Sequence Generation with Adaptive Compositional Modules (ACL2022) [paper]

  • Learngene: From Open-World to Your Learning Task (AAAI2022) [paper] [code]

  • Rethinking the Representational Continuity: Towards Unsupervised Continual Learning (ICLR2022) [paper]
  • Continual Learning with Filter Atom Swapping (ICLR2022) [paper]
  • Continual Learning with Recursive Gradient Optimization (ICLR2022) [paper]
  • TRGP: Trust Region Gradient Projection for Continual Learning (ICLR2022) [paper]
  • Looking Back on Learned Experiences For Class/task Incremental Learning (ICLR2022) [paper]
  • Continual Normalization: Rethinking Batch Normalization for Online Continual Learning (ICLR2022) [paper]
  • Model Zoo: A Growing Brain That Learns Continually (ICLR2022) [paper]
  • Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting (ICLR2022) [paper]
  • Memory Replay with Data Compression for Continual Learning (ICLR2022) [paper]
  • Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System (ICLR2022) [paper]
  • Online Coreset Selection for Rehearsal-based Continual Learning (ICLR2022) [paper]
  • Pretrained Language Model in Continual Learning: A Comparative Study (ICLR2022) [paper]
  • Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR2022) [paper]
  • New Insights on Reducing Abrupt Representation Change in Online Continual Learning (ICLR2022) [paper]
  • Towards Continual Knowledge Learning of Language Models (ICLR2022) [paper]
  • CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability (ICLR2022) [paper]
  • CoMPS: Continual Meta Policy Search (ICLR2022) [paper]
  • Information-theoretic Online Memory Selection for Continual Learning (ICLR2022) [paper]
  • Subspace Regularizers for Few-Shot Class Incremental Learning (ICLR2022) [paper]
  • LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 (ICLR2022) [paper]
  • Effect of scale on catastrophic forgetting in neural networks (ICLR2022) [paper]
  • Dataset Knowledge Transfer for Class-Incremental Learning without Memory (WACV2022) [paper]
  • Knowledge Capture and Replay for Continual Learning (WACV2022) [paper]
  • Online Continual Learning via Candidates Voting (WACV2022) [paper]

2021

  • Incremental Object Detection via Meta-Learning (TPAMI 2021) [paper] [code]
  • Triple-Memory Networks: A Brain-Inspired Method for Continual Learning (TNNLS 2021) [paper]
  • Memory efficient class-incremental learning for image classification (TNNLS 2021) [paper]
  • Class-Incremental Learning via Dual Augmentation (NeurIPS2021) [paper]
  • SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (NeurIPS2021) [paper]
  • RMM: Reinforced Memory Management for Class-Incremental Learning (NeurIPS2021) [paper]
  • Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima (NeurIPS2021) [paper]
  • Lifelong Domain Adaptation via Consolidated Internal Distribution (NeurIPS2021) [paper]
  • AFEC: Active Forgetting of Negative Transfer in Continual Learning (NeurIPS2021) [paper]
  • Natural continual learning: success is a journey, not (just) a destination (NeurIPS2021) [paper]
  • Gradient-based Editing of Memory Examples for Online Task-free Continual Learning (NeurIPS2021) [paper]
  • Optimizing Reusable Knowledge for Continual Learning via Metalearning (NeurIPS2021) [paper]
  • Formalizing the Generalization-Forgetting Trade-off in Continual Learning (NeurIPS2021) [paper]
  • Learning where to learn: Gradient sparsity in meta and continual learning (NeurIPS2021) [paper]
  • Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning (NeurIPS2021) [paper]
  • Posterior Meta-Replay for Continual Learning (NeurIPS2021) [paper]
  • Continual Auxiliary Task Learning (NeurIPS2021) [paper]
  • Mitigating Forgetting in Online Continual Learning with Neuron Calibration (NeurIPS2021) [paper]
  • BNS: Building Network Structures Dynamically for Continual Learning (NeurIPS2021) [paper]
  • DualNet: Continual Learning, Fast and Slow (NeurIPS2021) [paper]
  • BooVAE: Boosting Approach for Continual Learning of VAE (NeurIPS2021) [paper]
  • Generative vs. Discriminative: Rethinking The Meta-Continual Learning (NeurIPS2021) [paper]
  • Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (NeurIPS2021) [paper]
  • Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection (NeurIPS, 2021) [paper] [code]
  • SS-IL: Separated Softmax for Incremental Learning (ICCV, 2021) [paper]
  • Striking a Balance between Stability and Plasticity for Class-Incremental Learning (ICCV, 2021) [paper]
  • Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (ICCV, 2021) [paper]
  • Class-Incremental Learning for Action Recognition in Videos (ICCV, 2021) [paper]
  • Continual Prototype Evolution:Learning Online from Non-Stationary Data Streams (ICCV, 2021) [paper]
  • Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning (ICCV, 2021) [paper]
  • Co2L: Contrastive Continual Learning (ICCV, 2021) [paper]
  • Wanderlust: Online Continual Object Detection in the Real World (ICCV, 2021) [paper]
  • Continual Learning on Noisy Data Streams via Self-Purified Replay (ICCV, 2021) [paper]
  • Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data (ICCV, 2021) [paper]
  • Detection and Continual Learning of Novel Face Presentation Attacks (ICCV, 2021) [paper]
  • Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data (ICCV, 2021) [paper]
  • Continual Learning for Image-Based Camera Localization (ICCV, 2021) [paper]
  • Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting (ICCV, 2021) [paper]
  • Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (ICCV, 2021) [paper]
  • RECALL: Replay-based Continual Learning in Semantic Segmentation (ICCV, 2021) [paper]
  • Few-Shot and Continual Learning with Attentive Independent Mechanisms (ICCV, 2021) [paper]
  • Learning with Selective Forgetting (IJCAI, 2021) [paper]
  • Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
  • Kernel Continual Learning (ICML, 2021) [paper]
  • Variational Auto-Regressive Gaussian Processes for Continual Learning (ICML, 2021) [paper]
  • Bayesian Structural Adaptation for Continual Learning (ICML, 2021) [paper]
  • Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (ICML, 2021) [paper]
  • Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
  • Federated Continual Learning with Weighted Inter-client Transfer (ICML, 2021) [paper]
  • Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (NAACL, 2021) [paper]
  • Continual Learning for Text Classification with Information Disentanglement Based Regularization (NAACL, 2021) [paper]
  • CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (EMNLP, 2021) [paper][code]
  • Co-Transport for Class-Incremental Learning (ACM MM, 2021) [paper]
  • Towards Open World Object Detection (CVPR, 2021) [paper] [code] [video]
  • Prototype Augmentation and Self-Supervision for Incremental Learning (CVPR, 2021) [paper] [code]
  • ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning (CVPR, 2021) [paper]
  • Incremental Learning via Rate Reduction (CVPR, 2021) [paper]
  • IIRC: Incremental Implicitly-Refined Classification (CVPR, 2021) [paper]
  • Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning (CVPR, 2021) [paper]
  • Image De-raining via Continual Learning (CVPR, 2021) [paper]
  • Continual Learning via Bit-Level Information Preserving (CVPR, 2021) [paper]
  • Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation (CVPR, 2021) [paper]
  • Lifelong Person Re-Identification via Adaptive Knowledge Accumulation (CVPR, 2021) [paper]
  • Distilling Causal Effect of Data in Class-Incremental Learning (CVPR, 2021) [paper]
  • Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (CVPR, 2021) [paper]
  • Layerwise Optimization by Gradient Decomposition for Continual Learning (CVPR, 2021) [paper]
  • Adaptive Aggregation Networks for Class-Incremental Learning (CVPR, 2021) [paper]
  • Incremental Few-Shot Instance Segmentation (CVPR, 2021) [paper]
  • Efficient Feature Transformations for Discriminative and Generative Continual Learning (CVPR, 2021) [paper]
  • On Learning the Geodesic Path for Incremental Learning (CVPR, 2021) [paper]
  • Few-Shot Incremental Learning with Continually Evolved Classifiers (CVPR, 2021) [paper]
  • Rectification-based Knowledge Retention for Continual Learning (CVPR, 2021) [paper]
  • DER: Dynamically Expandable Representation for Class Incremental Learning (CVPR, 2021) [paper]
  • Rainbow Memory: Continual Learning with a Memory of Diverse Samples (CVPR, 2021) [paper]
  • Training Networks in Null Space of Feature Covariance for Continual Learning (CVPR, 2021) [paper]
  • Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (CVPR, 2021) [paper]
  • PLOP: Learning without Forgetting for Continual Semantic Segmentation (CVPR, 2021) [paper]
  • Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations (CVPR, 2021) [paper]
  • Online Class-Incremental Continual Learning with Adversarial Shapley Value(AAAI, 2021) [paper] [code]
  • Lifelong and Continual Learning Dialogue Systems: Learning during Conversation(AAAI, 2021) [paper]
  • Continual learning for named entity recognition(AAAI, 2021) [paper]
  • Using Hindsight to Anchor Past Knowledge in Continual Learning(AAAI, 2021) [paper]
  • Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network(AAAI, 2021) [paper] [code]
  • Curriculum-Meta Learning for Order-Robust Continual Relation Extraction(AAAI, 2021) [paper]
  • Continual Learning by Using Information of Each Class Holistically(AAAI, 2021) [paper]
  • Gradient Regularized Contrastive Learning for Continual Domain Adaptation(AAAI, 2021) [paper]
  • Unsupervised Model Adaptation for Continual Semantic Segmentation(AAAI, 2021) [paper]
  • A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation(AAAI, 2021) [paper]
  • Do Not Forget to Attend to Uncertainty While Mitigating Catastrophic Forgetting(WACV, 2021) [paper]

2020

  • Rethinking Experience Replay: a Bag of Tricks for Continual Learning(ICPR, 2020) [paper] [code]
  • Continual Learning for Natural Language Generation in Task-oriented Dialog Systems(EMNLP, 2020) [paper]
  • Distill and Replay for Continual Language Learning(COLING, 2020) [paper]
  • Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks (NeurIPS2020) [paper] [code]
  • Meta-Consolidation for Continual Learning (NeurIPS2020) [paper]
  • Understanding the Role of Training Regimes in Continual Learning (NeurIPS2020) [paper]
  • Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (NeurIPS2020) [paper]
  • Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning (NeurIPS2020) [paper]
  • Coresets via Bilevel Optimization for Continual Learning and Streaming (NeurIPS2020) [paper]
  • RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning (NeurIPS2020) [paper]
  • Continual Deep Learning by Functional Regularisation of Memorable Past (NeurIPS2020) [paper]
  • Dark Experience for General Continual Learning: a Strong, Simple Baseline (NeurIPS2020) [paper] [code]
  • GAN Memory with No Forgetting (NeurIPS2020) [paper]
  • Calibrating CNNs for Lifelong Learning (NeurIPS2020) [paper]
  • Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization (NeurIPS2020) [paper]
  • ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation(RecSys, 2020) [paper]
  • Initial Classifier Weights Replay for Memoryless Class Incremental Learning (BMVC2020) [paper]
  • Adversarial Continual Learning (ECCV2020) [paper] [code]
  • REMIND Your Neural Network to Prevent Catastrophic Forgetting (ECCV2020) [paper] [code]
  • Incremental Meta-Learning via Indirect Discriminant Alignment (ECCV2020) [paper]
  • Memory-Efficient Incremental Learning Through Feature Adaptation (ECCV2020) [paper]
  • PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (ECCV2020) [paper] [code]
  • Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference (ECCV2020) [paper]
  • Learning latent representions across multiple data domains using Lifelong VAEGAN (ECCV2020) [paper]
  • Online Continual Learning under Extreme Memory Constraints (ECCV2020) [paper]
  • Class-Incremental Domain Adaptation (ECCV2020) [paper]
  • More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning (ECCV2020) [paper]
  • Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation (ECCV2020) [paper]
  • GDumb: A Simple Approach that Questions Our Progress in Continual Learning (ECCV2020) [paper]
  • Imbalanced Continual Learning with Partitioning Reservoir Sampling (ECCV2020) [paper]
  • Topology-Preserving Class-Incremental Learning (ECCV2020) [paper]
  • GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems (CIKM2020) [paper]
  • OvA-INN: Continual Learning with Invertible Neural Networks (IJCNN2020) [paper]
  • XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning (ICLM2020) [paper]
  • Optimal Continual Learning has Perfect Memory and is NP-HARD (ICML2020) [paper]
  • Neural Topic Modeling with Continual Lifelong Learning (ICML2020) [paper]
  • Continual Learning with Knowledge Transfer for Sentiment Classification (ECML-PKDD2020) [paper] [code]
  • Semantic Drift Compensation for Class-Incremental Learning (CVPR2020) [paper] [code]
  • Few-Shot Class-Incremental Learning (CVPR2020) [paper]
  • Modeling the Background for Incremental Learning in Semantic Segmentation (CVPR2020) [paper]
  • Incremental Few-Shot Object Detection (CVPR2020) [paper]
  • Incremental Learning In Online Scenario (CVPR2020) [paper]
  • Maintaining Discrimination and Fairness in Class Incremental Learning (CVPR2020) [paper]
  • Conditional Channel Gated Networks for Task-Aware Continual Learning (CVPR2020) [paper]
  • Continual Learning with Extended Kronecker-factored Approximate Curvature (CVPR2020) [paper]
  • iTAML : An Incremental Task-Agnostic Meta-learning Approach (CVPR2020) [paper] [code]
  • Mnemonics Training: Multi-Class Incremental Learning without Forgetting (CVPR2020) [paper] [code]
  • ScaIL: Classifier Weights Scaling for Class Incremental Learning (WACV2020) [paper]
  • Accepted papers(ICLR2020) [paper]
  • Brain-inspired replay for continual learning with artificial neural networks (Natrue Communications 2020) [paper] [code]
  • Learning to Continually Learn (ECAI 2020) [paper] [code]

2019

  • Compacting, Picking and Growing for Unforgetting Continual Learning (NeurIPS2019)[paper][code]
  • Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning (ICMR2019) [paper][code]
  • Towards Training Recurrent Neural Networks for Lifelong Learning (Neural Computation 2019) [paper]
  • Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (IJCAI2019) [paper]
  • IL2M: Class Incremental Learning With Dual Memory (ICCV2019) [paper]
  • Incremental Learning Using Conditional Adversarial Networks (ICCV2019) [paper]
  • Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (KDD2019) [paper]
  • Random Path Selection for Incremental Learning (NeurIPS2019) [paper]
  • Online Continual Learning with Maximal Interfered Retrieval (NeurIPS2019) [paper]
  • Meta-Learning Representations for Continual Learning (NeurIPS2019) [paper] [code]
  • Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild (ICCV2019) [paper]
  • Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation (ICCV2019) [paper]
  • Lifelong GAN: Continual Learning for Conditional Image Generation (ICCV2019) [paper]
  • Continual learning of context-dependent processing in neural networks (Nature Machine Intelligence 2019) [paper] [code]
  • Large Scale Incremental Learning (CVPR2019) [paper] [code]
  • Learning a Unified Classifier Incrementally via Rebalancing (CVPR2019) [paper] [code]
  • Learning Without Memorizing (CVPR2019) [paper]
  • Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning (CVPR2019) [paper]
  • Task-Free Continual Learning (CVPR2019) [paper]
  • Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (ICML2019) [paper]
  • Efficient Lifelong Learning with A-GEM (ICLR2019) [paper] [code]
  • Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (ICLR2019) [paper] [code]
  • Overcoming Catastrophic Forgetting via Model Adaptation (ICLR2019) [paper]
  • A comprehensive, application-oriented study of catastrophic forgetting in DNNs (ICLR2019) [paper]

2018

  • Memory Replay GANs: learning to generate images from new categories without forgetting (NIPS2018) [paper] [code]
  • Reinforced Continual Learning (NIPS2018) [paper] [code]
  • Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting (NIPS2018) [paper]
  • Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (R-EWC) (ICPR2018) [paper] [code]
  • Exemplar-Supported Generative Reproduction for Class Incremental Learning (BMVC2018) [paper] [code]
  • End-to-End Incremental Learning (ECCV2018) [paper][code]
  • Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence (ECCV2018)[paper]
  • Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV2018) [paper] [code]
  • Memory Aware Synapses: Learning what (not) to forget (ECCV2018) [paper] [code]
  • Lifelong Learning via Progressive Distillation and Retrospection (ECCV2018) [paper]
  • PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR2018) [paper] [code]
  • Overcoming Catastrophic Forgetting with Hard Attention to the Task (ICML2018) [paper] [code]
  • Lifelong Learning with Dynamically Expandable Networks (ICLR2018) [paper]
  • FearNet: Brain-Inspired Model for Incremental Learning (ICLR2018) [paper]

2017

  • Incremental Learning of Object Detectors Without Catastrophic Forgetting (ICCV2017) [paper]
  • Overcoming catastrophic forgetting in neural networks (EWC) (PNAS2017) [paper] [code] [code]
  • Continual Learning Through Synaptic Intelligence (ICML2017) [paper] [code]
  • Gradient Episodic Memory for Continual Learning (NIPS2017) [paper] [code]
  • iCaRL: Incremental Classifier and Representation Learning (CVPR2017) [paper] [code]
  • Continual Learning with Deep Generative Replay (NIPS2017) [paper] [code]
  • Overcoming Catastrophic Forgetting by Incremental Moment Matching (NIPS2017) [paper] [code]
  • Expert Gate: Lifelong Learning with a Network of Experts (CVPR2017) [paper]
  • Encoder Based Lifelong Learning (ICCV2017) [paper]

2016

  • Learning without forgetting (ECCV2016) [paper] [code]

wbjeon2k

Pursuite for Progress.

Search