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Lifelong learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/WangTianduo/lifelong-learning-3-cases

Generative replay with feedback connections as a general strategy for continual learning

https://github.com/XSMUBC/DNC-lifelong-learning

Generative replay with feedback connections as a general strategy for continual learning

https://github.com/XSMUBC/Lifelong-learning_xsm

Generative replay with feedback connections as a general strategy for continual learning

https://github.com/aimagelab/mammoth

Efficient Lifelong Learning with A-GEM

https://github.com/alexlimh/DPR_MUF

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

https://github.com/blues-lin/fin-analyze

Learning to Continually Learn

https://github.com/bossdm/LifelongRL

Selective Experience Replay for Lifelong Learning

https://github.com/chandar-lab/IIRC

IIRC: Incremental Implicitly-Refined Classification

https://github.com/darpa-l2m/l2metrics

Lifelong Learning Metrics

https://github.com/darylfung96/differentiable_plasticity

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/enajx/ES

Meta-Learning through Hebbian Plasticity in Random Networks

https://github.com/ensta-u2is/torch-uncertainty

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

https://github.com/epsilon-deltta/ssd_guillotine

Lifelong Learning with Dynamically Expandable Networks

https://github.com/g-u-n/pycil

PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning

https://github.com/gahaalt/continual-learning-overview

Three scenarios for continual learning

https://github.com/gahaalt/continual-learning-with-hypernets

Three scenarios for continual learning

https://github.com/geraudnt/boolean_composition

A Boolean Task Algebra for Reinforcement Learning

https://github.com/giannifranchi/LP_BNN

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

https://github.com/howardhsu/L-DEM

Lifelong Domain Word Embedding via Meta-Learning

https://github.com/intel/openfl

Overcoming Forgetting in Federated Learning on Non-IID Data

https://github.com/jaehong-yoon93/DEN

Lifelong Learning with Dynamically Expandable Networks

https://github.com/jurastm/differentiable_neural_plasticity

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/lean-dojo/LeanAgent

LeanAgent: Lifelong Learning for Formal Theorem Proving

https://github.com/lifelong-ml/lpg-ftw

Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting

https://github.com/lifelong-robot-learning/libero

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

https://github.com/lifelong-robotic-vision/lifelong-object-recognition-challenge

CORe50: a New Dataset and Benchmark for Continuous Object Recognition

https://github.com/liuquande/SAML

A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

https://github.com/llzlcl/Continual-Learning

Generative replay with feedback connections as a general strategy for continual learning

https://github.com/maPiche/ANML-Project

Learning to Continually Learn

https://github.com/mako-anon/mako

Lifelong Learning with Dynamically Expandable Networks

https://github.com/microsoft/archai

Efficient Forward Architecture Search

https://github.com/mit-han-lab/mcunet

On-Device Training Under 256KB Memory

https://github.com/nikitadurasov/masksembles

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

https://github.com/p0werweirdo/tagfcl

Compacting, Picking and Growing for Unforgetting Continual Learning

https://github.com/rahafaljundi/MAS-Memory-Aware-Synapses

Memory Aware Synapses: Learning what (not) to forget

https://github.com/rasmusbergpalm/hebbian-evolution

Meta-Learning through Hebbian Plasticity in Random Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.