https://github.com/icoz69/cec-cvpr2021
Few-Shot Incremental Learning with Continually Evolved Classifiers
Papers with Code 아카이브
Continual Learning
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- 평가표
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- 보존된 평가 기록
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Continual Learning (also known as Incremental Learning, Life-long Learning) is a concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available anymore during training new ones.
If not mentioned, the benchmarks here are Task-CL, where task-id is provided on validation.
Source:
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Three scenarios for continual learning
Lifelong Machine Learning
Continual lifelong learning with neural networks: A review
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/iemprog/mimi
https://github.com/ihaeyong/SoftNet-3DLS
On the Soft-Subnetwork for Few-shot Class Incremental Learning
https://github.com/ilyassmoummad/ProtoCLR
https://github.com/imatge-upc/progressive_nns
https://github.com/intellhave/adaste
Training Binary Neural Networks using the Bayesian Learning Rule
https://github.com/ivclab/CondConvContinual
https://github.com/ivclab/PAE
https://github.com/jacobmontiel/river-tutorial-ijcai2020
https://github.com/jaryp/pixleattack
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
https://github.com/jiahuadong/fiss
No One Left Behind: Real-World Federated Class-Incremental Learning
https://github.com/jie-su/bpd
https://github.com/jmorrison57/ContinualLearningProject
https://github.com/jmribeiro/UGP
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning
https://github.com/johnr0/TaleBrush-backend
Generative and Discriminative Text Classification with Recurrent Neural Networks
https://github.com/juntingzh/incremental-learning-baselines
https://github.com/kamsyn95/CL_DNN
https://github.com/khashiii97/PNN
https://github.com/khashiii97/Progressive-Neural-Networks-for-IDS
https://github.com/kimhc6028/pathnet-pytorch
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
https://github.com/kimjeyoung/Mahalanobis-BERT
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
https://github.com/kkelchte/task_free_continual_learning
https://github.com/kuc2477/pytorch-deep-generative-replay
https://github.com/labmlai/annotated_deep_learning_paper_implementations
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online
https://github.com/lamda-cl/revisitingcil
https://github.com/lancopku/avg-avg
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
https://github.com/lit-leo/cgate
Conditional Channel Gated Networks for Task-Aware Continual Learning
https://github.com/liuyugeng/baadd
https://github.com/llzlcl/Continual-Learning
Generative replay with feedback connections as a general strategy for continual learning
https://github.com/lrzpellegrini/Fine-Grained-Continual-Learning
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
https://github.com/lrzpellegrini/Latent-Replay
https://github.com/lrzpellegrini/avalanche
https://github.com/lukashedegaard/structured-pruning-adapters
Efficient parametrization of multi-domain deep neural networks
https://github.com/lukinio/CL_CWAE
Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines
https://github.com/luulinh90s/CFD
Explaining How Deep Neural Networks Forget by Deep Visualization
https://github.com/luulinh90s/Contcap
ContCap: A scalable framework for continual image captioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.