https://github.com/IsaacRe/dynamic-representation-tracking
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
Papers with Code 아카이브
Continual Learning
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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/JH-LEE-KR/dualprompt-pytorch
DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
https://github.com/JH-LEE-KR/l2p-pytorch
https://github.com/Jin0316/Helpful-or-Harmful-Inter-Task-Association
https://github.com/KXY-PUBLIC/DiCGRL
https://github.com/Kabir5296/CSTA-CustomImplementation
CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning
https://github.com/King-Of-Knights/overcoming-catastrophic
https://github.com/Kostis-S-Z/mrcl_re
https://github.com/LTTM/IL-SemSegm
https://github.com/LeeGitaek/Kaggle_Solving
Dropout as an Implicit Gating Mechanism For Continual Learning
https://github.com/Liut2016/ecg-supcontrast
https://github.com/Lucasc-99/PackNet-Continual-Learning
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
https://github.com/Lucasc-99/packnet_cl
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
https://github.com/Mattdl/CLsurvey
A continual learning survey: Defying forgetting in classification tasks
https://github.com/MindCode-4/code-11/tree/main/EWC-Overcoming-catastrophic-forgetting
https://github.com/MindCode-4/code-6/tree/main/EWC-Overcoming-catastrophic-forgetting
https://github.com/MindSpore-scientific-2/code-12/tree/main/LGL-INR-master
https://github.com/MindSpore-scientific/code-2/tree/main/the-Soft-Nearest-Neighbor-Loss
A soft nearest-neighbor framework for continual semi-supervised learning
https://github.com/Minhchuyentoancbn/Continual-Learning
https://github.com/NixGD/variational-continual-learning
https://github.com/OREL-group/dBV
Continual Developmental Neurosimulation Using Embodied Computational Agents
https://github.com/PCJohn/cata-forget
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaperCodeReview/SupCL-TF
https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients
https://github.com/Piyush-555/VCL-in-PyTorch
https://github.com/ProsusAI/continual-object-instances
https://github.com/Ramos-Ramos/FlyModel
https://github.com/SAP-archive/machine-learning-dgm
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning
https://github.com/SAP/machine-learning-dgm
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning
https://github.com/SLrepo/residual_adapter
Efficient parametrization of multi-domain deep neural networks
https://github.com/Saraharas/A-Unifying-Bayesian-View-of-Continual-Learning
https://github.com/Saraharas/Continual-learning
https://github.com/SaynaEbrahimi/BayesianContinualLearning
Uncertainty-guided Continual Learning with Bayesian Neural Networks
https://github.com/SeptivianaSavitri/adl_python
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments
https://github.com/ThomasMiconi/LearningToLearnBOHP
Learning to learn with backpropagation of Hebbian plasticity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.