An Incremental Learning, Continual Learning, and Life-Long Learning Repository
Incremental Learning Repository: A collection of documents, papers, source code, and talks for incremental learning.
Keywords: Incremental Learning, Continual Learning, Continuous Learning, Lifelong Learning, Catastrophic Forgetting
CATALOGUE
Quick Start :sparkles: Survey :sparkles: Papers by Categories :sparkles: Datasets :sparkles: Tutorial, Workshop, & Talks
Competitions :sparkles: Awesome Reference :sparkles: Full Paper List
Continual Learning | Papers With Code
Incremental Learning | Papers With Code
Class Incremental Learning from the Past to Present by 思悥 | 知乎 (In Chinese)
A Little Survey of Incremental Learning | 知乎 (In Chinese)
Origin of the Study
Catastrophic Forgetting, Rehearsal and Pseudorehearsal(1995)[paper]
Catastrophic forgetting in connectionist networks(1999)[paper]
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem(1989)[paper]
Toolbox & Framework
[CLHive] [code]
[PTMPT] Prompt-based Incremental Learning Toolbox [code]
[LAMDA-PILOT] PILOT: A Pre-Trained Model-Based Continual Learning Toolbox(arXiv 2023)[paper][code]
[FACIL] Class-incremental learning: survey and performance evaluation on image classification(TPAMI 2022)[paper][code]
[Avalanche] Avalanche: An End-to-End Library for Continual Learning(CVPR 2021)[paper][code]
[PyCIL] PyCIL: A Python Toolbox for Class-Incremental Learning(arXiv 2021)[paper][code]
[Mammoth] An Extendible (General) Continual Learning Framework for Pytorch [code]
[PyContinual] An Easy and Extendible Framework for Continual Learning[code]
Books
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning(arXiv 2023)[github]
Deep Class-Incremental Learning: A Survey(arXiv 2023)[paper][code]
A Comprehensive Survey of Continual Learning: Theory, Method and Application(arxiv 2023)[paper]
[FACIL] Class-incremental learning: survey and performance evaluation on image classification(TPAMI 2022)[paper][code]
Online Continual Learning in Image Classification: An Empirical Survey (Neurocomputing 2021)[paper]
A continual learning survey: Defying forgetting in classification tasks (TPAMI 2021) [paper]
Rehearsal revealed: The limits and merits of revisiting samples in continual learning (ICCV 2021)[paper]
Continual Lifelong Learning in Natural Language Processing: A Survey (COLING 2020) [paper]
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (Neural Networks 2020) [paper]
Embracing Change: Continual Learning in Deep Neural Networks(Trends in Cognitive Sciences 2020)[paper]
Towards Continual Reinforcement Learning: A Review and Perspectives(arXiv 2020)[paper]
Class-incremental learning: survey and performance evaluation(arXiv 2020) [paper]
A comprehensive, application-oriented study of catastrophic forgetting in DNNs (ICLR 2019) [paper]
Three scenarios for continual learning (arXiv 2019) [paper]
Continual lifelong learning with neural networks: A review(arXiv 2019)[paper]
类别增量学习研究进展和性能评价 (自动化学报 2023)[paper]
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?(NeurIPS 2022)[paper]
[WPTP] A Theoretical Study on Solving Continual Learning(NeurIPS 2022)[paper][code]
The Challenges of Continuous Self-Supervised Learning(ECCV 2022)[peper]
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions(NeurIPS 2022)[paper]
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal(NeurIPS 2022)[paper][code]
Exploring Example Influence in Continual Learning(NeurIPS 2022)[paper]
Biological underpinnings for lifelong learning machines(Nat. Mach. Intell. 2022)[paper]
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning(CVPR 2022)[paper][code]
[OpenLORIS-Object] Towards Lifelong Object Recognition: A Dataset and Benchmark(Pattern Recognit 2022)[paper]
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning (CVPR 2022) [paper]
Learngene: From Open-World to Your Learning Task (AAAI 2022) [paper]
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning (ICLR 2022) [paper]
[CLEVA-Compass] CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability (ICLR 2022) [paper][code]
Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting (ICLR 2022) [paper]
[CKL] Towards Continual Knowledge Learning of Language Models (ICLR 2022) [paper]
Pretrained Language Model in Continual Learning: A Comparative Study (ICLR 2022) [paper]
Effect of scale on catastrophic forgetting in neural networks (ICLR 2022) [paper]
LifeLonger: A Benchmark for Continual Disease Classification(arXiv 2022)[paper]
[CDDB] A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials(arXiv 2022)[paper]
[BN Tricks] Diagnosing Batch Normalization in Class Incremental Learning(arXiv 2022)[paper]
Architecture Matters in Continual Learning(arXiv 2022)[paper]
Learning where to learn: Gradient sparsity in meta and continual learning(NeurIPS 2021) [paper]
Continuous Coordination As a Realistic Scenario for Lifelong Learning(ICML 2021)[paper]
Understanding the Role of Training Regimes in Continual Learning (NeurIPS 2020)[paper]
Optimal Continual Learning has Perfect Memory and is NP-HARD (ICML 2020)[paper]
[FSCIL] Few-shot Class Incremental Learning [Link]
[DCIL] Decentralized Class Incremental Learning [paper][Setting]
Tips: you can use ctrl+F to match abbreviations with articles, or browse the paper list below.
Network Structure | Rehearsal | |
---|---|---|
2023 | A-Prompts (arXiv 2023)[paper] ESN(AAAI 2023)[paper][code] RevisitingCIL(arXiv 2023)[paper][code] LwP(ICLR 2023)[paper] SDMLP(ICLR 2023)[paper] SaLinA(ICLR 2023)[paper][code] BEEF(ICLR 2023)[paper][code] WaRP(ICLR 2023)[paper] OBC(ICLR 2023)[paper] NC-FSCIL(ICLR 2023)[paper][code] iVoro(ICLR 2023)[paper] DAS(ICLR 2023)[paper] Progressive Prompts(ICLR 2023)[paper] SDP(ICLR 2023)[paper][code] iLDR(ICLR 2023)[paper] SoftNet-FSCIL(ICLR 2023)[paper][code] PAR(CVPR 2023)[paper] PETAL(CVPR 2023)[paper][code] SAVC(CVPR 2023)[paper][code] CODA-Prompt(CVPR 2023)[paper][code] |
FeTrIL(WACV 2023)[paper][code] ESMER(ICLR 2023)[paper][code] MEMO(ICLR 2023)[paper][code] CUDOS(ICLR 2023)[paper] ACGAN(ICLR 2023)[paper][code] TAMiL(ICLR 2023)[paper][code] RSOI(CVPR 2023)[paper][code] TBBN(CVPR 2023)[paper] AMSS(CVPR 2023)[paper] DGCL(CVPR 2023)[paper] PCR(CVPR 2023)[paper][code] FMWISS(CVPR 2023)[paper] CL-DETR(CVPR 2023)[paper][code] PIVOT(CVPR 2023)[paper] CIM-CIL(CVPR 2023)[paper][code] DNE(CVPR 2023)[paper] |
2022 | RD-IOD(ACM Trans 2022)[paper] NCM(arXiv 2022)[paper] IPP(arXiv 2022)[paper] Incremental-DETR(arXiv 2022)[paper] ELI(CVPR 2022)[paper] CASSLE(CVPR 2022)[paper][code] iFS-RCNN(CVPR 2022)[paper] WILSON(CVPR 2022)[paper][code] Connector(CVPR 2022)[paper][code] PAD(CVPR 2022)[paper] ERD(CVPR 2022)[paper][code] AFC(CVPR 2022)[paper][code] FACT(CVPR 2022)[paper][code] L2P(CVPR 2022)[paper][code] MEAT(CVPR 2022)[paper][code] RCIL(CVPR 2022)[paper][code] ZITS(CVPR 2022)[paper][code] MTPSL(CVPR 2022)[paper][code] MMA(CVPR-Workshop 2022)[paper] CoSCL(ECCV 2022)[paper][code] AdNS(ECCV 2022)[paper] ProCA(ECCV 2022)[paper][code] R-DFCIL(ECCV 2022)[paper][code] S3C(ECCV 2022)[paper][code] H^2^(ECCV 2022)[paper] DualPrompt(ECCV 2022)[paper] ALICE(ECCV 2022)[paper][code] RU-TIL(ECCV 2022)[paper][code] FOSTER(ECCV 2022)[paper] SSR(ICLR 2022)[paper][code] RGO(ICLR 2022)[paper] TRGP(ICLR 2022)[paper] AGCN(ICME 2022)[paper][code] WSN(ICML 2022)[paper][code] NISPA(ICML 2022)[paper][code] S-FSVI(ICML 2022)[paper][code] CUBER(NeurIPS 2022)[paper] ADA(NeurIPS 2022)[paper] CLOM(NeurIPS 2022)[paper] S-Prompt(NeurIPS 2022)[paper] ALIFE(NIPS 2022)[paper] PMT(NIPS 2022)[paper] STCISS(TNNLS 2022)[paper] DSN(TPAMI 2022)[paper] MgSvF(TPAMI 2022)[paper] TransIL(WACV 2022)[paper] |
NER-FSCIL(ACL 2022)[paper] LIMIT(arXiv 2022)[paper] EMP(arXiv 2022)[paper] SPTM(CVPR 2022)[paper] BER(CVPR 2022)[paper] Sylph(CVPR 2022)[paper] MetaFSCIL(CVPR 2022)[paper] FCIL(CVPR 2022)[paper][code] FILIT(CVPR 2022)[paper] PuriDivER(CVPR 2022)[paper][code] SNCL(CVPR 2022)[paper] DVC(CVPR 2022)[paper][code] CVS(CVPR 2022)[paper] CPL(CVPR 2022)[paper] GCR(CVPR 2022)[paper] LVT(CVPR 2022)[paper] vCLIMB(CVPR 2022)[paper][code] Learn-to-Imagine(CVPR 2022)[paper][code] DCR(CVPR 2022)[paper] DIY-FSCIL(CVPR 2022)[paper] C-FSCIL(CVPR 2022)[paper][code] SSRE(CVPR 2022)[paper] CwD(CVPR 2022)[paper][code] MSL(CVPR 2022)[paper] DyTox(CVPR 2022)[paper][code] X-DER(ECCV 2022)[paper] clsss-iNCD(ECCV 2022)[paper][code] ARI(ECCV 2022)[paper][code] Long-Tailed-CIL(ECCV 2022)[paper][code] LIRF(ECCV 2022)[paper] DSDM(ECCV 2022)[paper][code] CVT(ECCV 2022)[paper] TwF(ECCV 2022)[paper][code] CSCCT(ECCV 2022)[paper][code] DLCFT(ECCV 2022)[paper] ERDR(ECCV2022)[paper] NCDwF(ECCV2022)[paper] CoMPS(ICLR 2022)[paper] i-fuzzy(ICLR 2022)[paper][code] CLS-ER(ICLR 2022)[paper][code] MRDC(ICLR 2022)[paper][code] OCS(ICLR 2022)[paper] InfoRS(ICLR 2022)[paper] ER-AML(ICLR 2022)[paper][code] FAS(ICLR 2022)[paper] LUMP(ICLR 2022)[paper] CF-IL(ICLR 2022)[paper][code] LFPT5(ICLR 2022)[paper][code] Model Zoo(ICLR 2022)[paper] OCM(ICML 2022)[paper][code] DRO(ICML 2022)[paper][code] EAK(ICPR 2022)[paper] RAR(NeurIPS 2022)[paper] LiDER(NeurIPS 2022)[paper] SparCL(NeurIPS 2022)[paper] ClonEx-SAC(NeurIPS 2022)[paper] ODDL(NeurIPS 2022)[paper] CSSL(PRL 2022)[paper] MBP(TNNLS 2022)[paper] CandVot(WACV 2022)[paper] FlashCards(WACV 2022)[paper] |
2021 | Meta-DR(CVPR 2021)[paper] continual cross-modal retrieval(CVPR 2021)[paper] DER(CVPR 2021)[paper][code] EFT(CVPR 2021)[paper][code] PASS(CVPR 2021)[paper][code] GeoDL(CVPR 2021)[paper][code] IL-ReduNet(CVPR 2021)[paper] PIGWM(CVPR 2021)[paper] BLIP(CVPR 2021)[paper][code] Adam-NSCL(CVPR 2021)[paper][code] PLOP(CVPR 2021)[paper][code] SDR(CVPR 2021)[paper][code] SKD(CVPR 2021)[paper] Always Be Dreaming(ICCV 2021)[paper][code] SPB(ICCV 2021)[paper] Else-Net(ICCV 2021)[paper] LCwoF-Framework(ICCV 2021)[paper] AFEC(NeurIPS 2021)[paper][code] F2M(NeurIPS 2021)[paper][code] NCL(NeurIPS 2021)[paper][code] BCL(NeurIPS 2021)[paper][code] Posterior Meta-Replay(NeurIPS 2021)[paper] MARK(NeurIPS 2021)[paper][code] Co-occur(NeurIPS 2021)[paper][code] LINC(AAAI 2021)[paper] CLNER(AAAI 2021)[paper] CLIS(AAAI 2021)[paper] PCL(AAAI 2021)[paper] MAS3(AAAI 2021)[paper] FSLL(AAAI 2021)[paper] VAR-GPs(ICML 2021)[paper] BSA(ICML 2021)[paper] GPM(ICLR 2021)[paper][code] |
TMN(TNNLS 2021)[paper] RKD(AAAI 2021)[paper] AANets(CVPR 2021)[paper][code] ORDisCo(CVPR 2021)[paper] DDE(CVPR 2021)[paper][code] IIRC(CVPR 2021)[paper] Hyper-LifelongGAN(CVPR 2021)[paper] CEC(CVPR 2021)[paper] iMTFA(CVPR 2021)[paper] RM(CVPR 2021)[paper] LOGD(CVPR 2021)[paper] SPPR(CVPR 2021)[paper] LReID(CVPR 2021)[paper][code] SS-IL(ICCV 2021)[paper] TCD(ICCV 2021)[paper] CLOC(ICCV 2021)[paper][code] CoPE(ICCV 2021)[paper][code] Co2L(ICCV 2021)[paper][code] SPR(ICCV 2021)[paper] NACL(ICCV 2021)[paper] CL-HSCNet(ICCV 2021)[paper][code] RECALL(ICCV 2021)[paper][code] VAE(ICCV 2021)[paper] ERT(ICPR 2021)[paper][code] KCL(ICML 2021)[paper][code] MLIOD(TPAMI 2021)[paper][code] BNS(NeurIPS 2021)[paper] FS-DGPM(NeurIPS 2021)[paper] SSUL(NeurIPS 2021)[paper] DualNet(NeurIPS 2021)[paper] classAug(NeurIPS 2021)[paper] GMED(NeurIPS 2021)[paper] BooVAE(NeurIPS 2021)[paper][code] GeMCL(NeurIPS 2021)[paper] RMM(NIPS 2021)[paper][code] LSF(IJCAI 2021)[paper] ASER(AAAI 2021)[paper][code] CML(AAAI 2021)[paper][code] HAL(AAAI 2021)[paper] MDMT(AAAI 2021)[paper] AU(WACV 2021)[paper] IDBR(NAACL 2021)[paper][code] COIL(ACM MM 2021)[paper] |
2020 | CWR*(CVPR 2020)[paper] MiB(CVPR 2020)[paper][code] K-FAC(CVPR 2020)[paper] SDC(CVPR 2020)[paper][code] NLTF(AAAI 2020) [paper] CLCL(ICLR 2020)[paper][code] APD(ICLR 2020)[paper] HYPERCL(ICLR 2020)[paper][code] CN-DPM(ICLR 2020)[paper] UCB(ICLR 2020)[paper][code] CLAW(ICLR 2020)[paper] CAT(NeurIPS 2020)[paper][code] AGS-CL(NeurIPS 2020)[paper] MERLIN(NeurIPS 2020)[paper][code] OSAKA(NeurIPS 2020)[paper][code] RATT(NeurIPS 2020)[paper] CCLL(NeurIPS 2020)[paper] CIDA(ECCV 2020)[paper] GraphSAIL(CIKM 2020)[paper] ANML(ECAI 2020)[paper][code] ICWR(BMVC 2020)[paper] DAM(TPAMI 2020)[paper] OGD(PMLR 2020)[paper] MC-OCL(ECCV2020)[paper][code] RCM(ECCV 2020)[paper][code] OvA-INN(IJCNN 2020)[paper] XtarNet(ICLM 2020)[paper][code] DMC(WACV 2020)[paper] |
iTAML(CVPR 2020)[paper][code] FSCIL(CVPR 2020)[paper][code] GFR(CVPR 2020)[paper][code] OSIL(CVPR 2020)[paper] ONCE(CVPR 2020)[paper] WA(CVPR 2020)[paper][code] CGATE(CVPR 2020)[paper][code] Mnemonics Training(CVPR 2020)[paper][code] MEGA(NeurIPS 2020)[paper] GAN Memory(NeurIPS 2020)[paper][code] Coreset(NeurIPS 2020)[paper] FROMP(NeurIPS 2020)[paper][code] DER(NeurIPS 2020)[paper][code] InstAParam(NeurIPS 2020)[paper] BOCL(AAAI 2020)[paper] REMIND(ECCV 2020)[paper][code] ACL(ECCV 2020)[paper][code] TPCIL(ECCV 2020)[paper] GDumb(ECCV 2020)[paper][code] PRS(ECCV 2020)[paper] PODNet(ECCV 2020)[paper][code] FA(ECCV 2020)[paper] L-VAEGAN(ECCV 2020)[paper] Piggyback GAN(ECCV 2020)[paper][code] IDA(ECCV 2020)[paper] RCM(ECCV 2020)[paper] LAMOL(ICLR 2020)[paper][code] FRCL(ICLR 2020)[paper][code] GRS(ICLR 2020)[paper] Brain-inspired replay(Natrue Communications 2020)[paper][code] CLIFER(FG 2020)[paper] ScaIL(WACV 2020)[paper][code] ARPER(EMNLP 2020)[paper] DnR(COLING 2020)[paper] ADER(RecSys 2020)[paper][code] MUC(ECCV 2020)[paper][code] |
2019 | LwM(CVPR 2019)[paper] CPG(NeurIPS 2019)[paper][code] UCL(NeurIPS 2019)[paper] OML(NeurIPS 2019)[paper][code] ALASSO(ICCV 2019)[paper] Learn-to-Grow(PMLR 2019)[paper] OWM(Nature Machine Intelligence 2019)[paper][code] |
LUCIR(CVPR 2019)[paper][code] TFCL(CVPR 2019)[paper] GD(CVPR 2019)[paper][code] DGM(CVPR 2019)[paper] BiC(CVPR 2019)[paper][code] MER(ICLR 2019)[paper][code] PGMA(ICLR 2019)[paper] A-GEM(ICLR 2019)[paper][code] IL2M(ICCV 2019)[paper] ILCAN(ICCV 2019)[paper] Lifelong GAN(ICCV 2019)[paper] GSS(NIPS 2019)[paper] ER(NIPS 2019)[paper] MIR(NIPS 2019)[paper][code] RPS-Net(NIPS 2019)[paper] CLEER(IJCAI 2019)[paper] PAE(ICMR 2019)[paper][code] |
2018 | PackNet(CVPR 2018)[paper][code] OLA(NIPS 2018)[paper] RCL(NIPS 2018)[paper][code] MARL(ICLR 2018)[paper] DEN(ICLR 2018)[paper][code] P&C(ICML 2018)[paper] Piggyback(ECCV 2018)[paper][code] RWalk(ECCV 2018)[paper] MAS(ECCV 2018)[paper][code] R-EWC(ICPR 2018)[paper][code] HAT(PMLR 2018)[paper][code] |
MeRGANs(NIPS 2018)[paper][code] EEIL(ECCV 2018)[paper][code] Adaptation by Distillation(ECCV 2018)[paper] ESGR(BMVC 2018)[paper][code] VCL(ICLR 2018)[paper] FearNet(ICLR 2018)[paper] DGDMN(ICLR 2018)[paper] |
2017 | Expert Gate(CVPR 2017)[paper][code] ILOD(ICCV 2017)[paper][code] EBLL(ICCV2017)[paper] IMM(NIPS 2017)[paper][code] SI(ICML 2017)[paper][code] EWC(PNAS 2017)[paper][code] |
iCARL(CVPR 2017)[paper][code] GEM(NIPS 2017)[paper][code] DGR(NIPS 2017)[paper][code] |
2016 | LwF(ECCV 2016)[paper][code] |
Data decentralized incremental learning
Data centralized incremental learning
All other studies aforementioned except those already in the 'Decentralized' section.
datasets | describes |
---|---|
ImageNet | There are 1.28 million training images and 50,000 validation images in over 1,000 categories. Usually crop into 224×224 color image |
TinyImageNet | Contains 100,000 64×64 color images of 200 categories (500 per category). Each class has 500 training images, 50 validation images, and 50 test images. |
MiniImageNet | This dataset is a subset of ImageNet used for few-shot learning. It consists of 60, 000 colour images of size 84 × 84 with 100 classes, each having 600 examples. |
SubImageNet | This dataset is a 100-class subset of ImageNet's random sample, which contains approximately 130,000 images for training and 5,000 images for testing. |
CIFAR-10/100 | Both datasets contain 60,000 natural RGB images of the size 32 × 32, including 50,000 training and 10,000 test images. CIFAR10 has 10 classes, while CIFAR100 has 100 classes. |
CORe50 | This dataset consists of 164,866 128×128 RGB-D images: 11 sessions × 50 objects × (around 300) frames per session. Github CORe50: a New Dataset and Benchmark for Continuous Object Recognition |
OpenLORIS-Object | This is the first real-world dataset for robotic vision with independent and quantifiable environmental factors, compared with other lifelong learning datasets, with 186 instances, 63 categories and 2,138,050 images. |
Life-Long learning | 李宏毅
Life-long Learning: [ppt] [pdf]
Catastrophic Forgetting [Chinese] [English]
Mitigating Catastrophic Forgetting [Chinese] [English]
Meta Learning : Learn to Learn [Chinese]
Continual AI Lecture
Open World Lifelong Learning | A Continual Machine Learning Course
Prompting-based Continual Learning | Continual AI
VALSE Webinar (In Chinese)
20211215【学无止境:深度连续学习】洪晓鹏:记忆拓扑保持的深度增量学习方法
20211215【学无止境:深度连续学习】李玺:基于深度神经网络的持续性学习理论与方法
ACM MULTIMEDIA
ACM2021 Few-shot Learning for Multi-Modality Tasks
CVPR Workshop
CVPR 2022 Workshop on Continual Learning in Computer Vision
CVPR2021 Workshop on Continual Learning in Computer Vision
CVPR2020 Workshop on Continual Learning in Computer Vision
CVPR2017 Continuous and Open-Set Learning Workshop
ICML Tutorial/Workshop
ICML 2021 Workshop on Theory and Foundation of Continual Learning
ICML 2021 Tutorial on Continual Learning with Deep Architectures
ICML2020 Workshop on Continual Learning
NeurIPS Workshop
NeurIPS2021 4th Robot Learning Workshop: Self-Supervised and Lifelong Learning
NeurIPS2018 Continual learning Workshop
NeurIPS2016 Continual Learning and Deep Networks Workshop
IJCAI Workshop
IJCAI 2021 International Workshop on Continual Semi-Supervised Learning
ContinualAI wiki
A Non-profit Research Organization and Open Community on Continual Learning for AI
CoLLAs
Conference on Lifelong Learning Agents - CoLLAs 2022
achieved
3rd CLVISION CVPR Workshop Challenge 2022
IJCAI 2021 - International Workshop on Continual Semi-Supervised Learning
2rd CLVISION CVPR Workshop Challenge 2021
1rd CLVISION CVPR Workshop Challenge 2020
[1] https://github.com/xialeiliu/Awesome-Incremental-Learning
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PRD Prototype-Sample Relation Distillation: Towards Replay-FreeContinual Learning(ICML 2023) [paper]
A Unified Continual Learning Framework with General Parameter-Efficient Tuning(ICCV 2023) [paper][code]
Cross-Modal Alternating Learning with Task-Aware Representations for Continual Learning(TMM 2023) [paper][code]
Semantic Knowledge Guided Class-Incremental Learning(TCSVT 2023) [paper]
Non-Exemplar Class-Incremental Learning via Adaptive Old Class Reconstruction(ACM MM 2023) [paper][code]
HiDe-Prompt Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality(NeurIPS 2023)[paper][code]
TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion(NeurIPS 2023)[paper]
AdaB2N Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation(NeurIPS 2023)[paper][[code]]](https://github.com/lvyilin/AdaB2N)
Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning(ICCV 2023)[paper]
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering(ICCV 2023)[paper]
Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation for Non-Exemplar Class-Incremental Learning(ICCV 2023)[paper]
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining(ICCV 2023)[paper]
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision(ICCV 2023)[paper]
Dynamic Residual Classifier for Class Incremental Learning(ICCV 2023)[paper]
Audio-Visual Class-Incremental Learning(ICCV 2023)[paper]
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning(ICCV 2023)[paper]
Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental Learning(ICCV 2023)[paper]
Heterogeneous Forgetting Compensation for Class-Incremental Learning(ICCV 2023)[paper]
Masked Autoencoders are Efficient Class Incremental Learners(ICCV 2023)[paper]
Knowledge Restore and Transfer for Multi-Label Class-Incremental Learning(ICCV 2023)[paper]
Space-time Prompting for Video Class-incremental Learning(ICCV 2023)[paper]
CLNeRF: Continual Learning Meets NeRF(ICCV 2023)[paper]
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?(ICCV 2023)[paper]
Exemplar-Free Continual Transformer with Convolutions(ICCV 2023)[paper]
Self-Evolved Dynamic Expansion Model for Task-Free Continual Learning(ICCV 2023)[paper]
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision(ICCV 2023)[paper]
Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning(ICCV 2023)[paper]
Measuring Asymmetric Gradient Discrepancy in Parallel Continual Learning(ICCV 2023)[paper]
Wasserstein Expansible Variational Autoencoder for Discriminative and Generative Continual Learning(ICCV 2023)[paper]
Data Augmented Flatness-aware Gradient Projection for Continual Learning(ICCV 2023)[paper]
A Unified Continual Learning Framework with General Parameter-Efficient Tuning(ICCV 2023)[paper]
Introducing Language Guidance in Prompt-based Continual Learning(ICCV 2023)[paper]
Continual Learning for Personalized Co-speech Gesture Generation(ICCV 2023)[paper]
Growing a Brain with Sparsity-Inducing Generation for Continual Learning(ICCV 2023)[paper]
Towards Realistic Evaluation of Industrial Continual Learning Scenarios with an Emphasis on Energy Consumption and Computational Footprint(ICCV 2023)[paper]
Class-Incremental Grouping Network for Continual Audio-Visual Learning(ICCV 2023)[paper]
ICICLE: Interpretable Class Incremental Continual Learning(ICCV 2023)[paper]
Online Prototype Learning for Online Continual Learning(ICCV 2023)[paper]
NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual Learning(ICCV 2023)[paper]
Few-shot Continual Infomax Learning(ICCV 2023)[paper]
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model(ICCV 2023)[paper]
Instance and Category Supervision are Alternate Learners for Continual Learning(ICCV 2023)[paper]
Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models(ICCV 2023)[paper]
CLR: Channel-wise Lightweight Reprogramming for Continual Learning(ICCV 2023)[paper]
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning(ICCV 2023)[paper]
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation(ICCV 2023)[paper]
CBA: Improving Online Continual Learning via Continual Bias Adaptor(ICCV 2023)[paper]
Continual Zero-Shot Learning through Semantically Guided Generative Random Walks(ICCV 2023)[paper]
A Soft Nearest-Neighbor Framework for Continual Semi-Supervised Learning(ICCV 2023)[paper]
Online Continual Learning on Hierarchical Label Expansion(ICCV 2023)[paper]
Investigating the Catastrophic Forgetting in Multimodal Large Language Models (NeurIPS Workshop 23) [paper]
Generating Instance-level Prompts for Rehearsal-free Continual Learning(ICCV 2023)[paper]
Heterogeneous Continual Learning(CVPR 2023)[paper]
Partial Hypernetworks for Continual Learning(CoLLAs 2023)[paper]
Learnability and Algorithm for Continual Learning(ICML 2023)[paper]
Parameter-Level Soft-Masking for Continual Learning(ICML 2023)[paper]
Improving Online Continual Learning Performance and Stability with Temporal Ensembles(CoLLAs 2023)[paper]
Exploring Continual Learning for Code Generation Models(ACL 2023)[paper]
[Fed-CPrompt] Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning(FL-ICML 2023)[paper]
Online Continual Learning for Robust Indoor Object Recognition(ICCV 2023)[paper]
Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery(ICCV 2023)[paper]
[XLDA] XLDA: Linear Discriminant Analysis for Scaling Continual Learning to Extreme Classification at the Edge[ICML 2023][paper]
[CLR] CLR: Channel-wise Lightweight Reprogramming for Continual Learning(ICCV 2023)[paper]
[CS-VQLA] Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery(MICCAI 2023)[paper][code]
Online Prototype Learning for Online Continual Learning(ICCV 2023)[paper][code]
Cost-effective On-device Continual Learning over Memory Hierarchy with Miro(ACM MobiCom 23)[paper]
[CBA] CBA: Improving Online Continual Learning via Continual Bias Adaptor(ICCV 2023)[paper]
[A-Prompts] Remind of the Past: Incremental Learning with Analogical Prompts(arXiv 2023)[paper]
[ESN] Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference(AAAI 2023)[paper][code]
[RevisitingCIL] Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need(arXiv 2023)[paper][code]
[LwP] Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting(ICLR 2023)[paper]
[SDMLP] Sparse Distributed Memory is a Continual Learner(ICLR 2023)[paper]
[SaLinA] Building a Subspace of Policies for Scalable Continual Learning(ICLR 2023)[paper][code]
[BEEF] BEEF:Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion(ICLR 2023)[paper][code]
[WaRP] Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning(ICLR 2023)[paper]
[OBC] Online Bias Correction for Task-Free Continual Learning(ICLR 2023)[paper]
[NC-FSCIL] Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning(ICLR 2023)[paper][code]
[iVoro] Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning(ICLR 2023)[paper]
[DAS] Continual Learning of Language Models(ICLR 2023)[paper]
[Progressive Prompts] Progressive Prompts: Continual Learning for Language Models without Forgetting(ICLR 2023)[paper]
[SDP] Online Boundary-Free Continual Learning by Scheduled Data Prior(ICLR 2023)[paper][code]
[iLDR] Incremental Learning of Structured Memory via Closed-Loop Transcription(ICLR 2023)[paper]
[SoftNet-FSCIL] On the Soft-Subnetwork for Few-Shot Class Incremental Learning On the Soft-Subnetwork for Few-Shot Class Incremental Learning(ICLR 2023)[paper][code]
[ESMER] Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning(ICLR 2023)[paper][code]
[MEMO] A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning(ICLR 2023)[paper][code]
[CUDOS] Continual Unsupervised Disentangling of Self-Organizing Representations(ICLR 2023)[paper]
[ACGAN] Better Generative Replay for Continual Federated Learning(ICLR 2023)[paper][code]
[TAMiL] Task-Aware Information Routing from Common Representation Space in Lifelong Learning(ICLR 2023)[paper][code]
[FeTrIL] Feature Translation for Exemplar-Free Class-Incremental Learning(WACV 2023)[paper][code]
[RSOI] Regularizing Second-Order Influences for Continual Learning(CVPR 2023)[paper][code]
[TBBN] Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning(CVPR 2023)[paper]
[AMSS] Continual Semantic Segmentation with Automatic Memory Sample Selection(CVPR 2023)[paper]
[DGCL] Exploring Data Geometry for Continual Learning(CVPR 2023)[paper]
[PCR] PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning(CVPR 2023)[paper][code]
[FMWISS] Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation(CVPR 2023)[paper]
[CL-DETR] Continual Detection Transformer for Incremental Object Detection(CVPR 2023)[paper][code]
[PIVOT] PIVOT: Prompting for Video Continual Learning(CVPR 2023)[paper]
[CIM-CIL] Class-Incremental Exemplar Compression for Class-Incremental Learning(CVPR 2023)[paper][code]
[DNE] Dense Network Expansion for Class Incremental Learning(CVPR 2023)[paper]
[PAR] Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning(CVPR 2023)[paper]
[PETAL] A Probabilistic Framework for Lifelong Test-Time Adaptation(CVPR 2023)[paper][code]
[SAVC] Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning(CVPR 2023)[paper][code]
[CODA-Prompt] CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning(CVPR 2023)[paper][code]