https://github.com/cranial-xix/bome
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Papers with Code 아카이브
Continual Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/csiplab/task-increment-rank-update
https://github.com/cvlab-yonsei/dkd
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
https://github.com/dalxndr/NEURO140
https://github.com/danielkelshaw/RadialBNN
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
https://github.com/danielkelshaw/WeightUncertainty
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
https://github.com/daskol/paper-reviews
https://github.com/david-knigge/overcoming-catastrophic-forgetting-fncm-uva
https://github.com/davidczy/supcon_gamma
https://github.com/delyan-boychev/grad-cache-con-learning
https://github.com/diegomarron/ElasticSwapRandomForest
Resource-aware Elastic Swap Random Forest for Evolving Data Streams
https://github.com/donlee90/icarl
https://github.com/e-solutions-GmbH/TensorFlow-iEVM
Exploring the Open World Using Incremental Extreme Value Machines
https://github.com/epsilon-deltta/ssd_guillotine
https://github.com/etrontech/vfmseg
Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
https://github.com/facebookresearch/minihack
The Effectiveness of World Models for Continual Reinforcement Learning
https://github.com/fanlyu/ttd
PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery
https://github.com/fcdl94/MiB
Modeling the Background for Incremental Learning in Semantic Segmentation
https://github.com/feifeiobama/rewireneuron
https://github.com/fionisarnen/Lifelong-Learning-churn-prediction-torch
https://github.com/flyingsheepbin/pet-biometrics
https://github.com/forcesh/SupContrast
https://github.com/gahaalt/continual-learning-overview
https://github.com/gahaalt/continual-learning-with-hypernets
https://github.com/gauthambekal93/hvac_continual_rl
https://github.com/geox-lab/cmn
https://github.com/germank/CALM
https://github.com/guerbet-ai/wsp-contrastive
https://github.com/h3lio5/episodic-lifelong-learning
https://github.com/hannaiiyanggit/unicon
https://github.com/haseebs/Pseudo-rehearsal-Incremental-Learning
https://github.com/hooman650/supcl-seq
https://github.com/hshustc/CVPR19_Incremental_Learning
https://github.com/htt210/CatastrophicGANCode
On Catastrophic Forgetting and Mode Collapse in Generative Adversarial Networks
https://github.com/hursung1/DeepGenerativeReplay
https://github.com/hursung1/GradientEpisodicMemory
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.