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Incremental Learning

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보존된 평가 기록: 112

Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.

코드

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

https://github.com/nkatzz/iled

Incremental Learning of Event Definitions with Inductive Logic Programming

https://github.com/numenta/NAB

Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection

https://github.com/nvshrao/Elastic-Weight-Consolidation

Overcoming catastrophic forgetting in neural networks

https://github.com/p0werweirdo/tagfcl

Compacting, Picking and Growing for Unforgetting Continual Learning

https://github.com/rokmr/TACLE

TACLE: Task and Class-aware Exemplar-free Semi-supervised Class Incremental Learning

https://github.com/sairin1202/BIC

Large Scale Incremental Learning

https://github.com/shivamsaboo17/overcoming-catastrophic-forgetting-in-neural-networks

Overcoming catastrophic forgetting in neural networks

https://github.com/sjtu-quant/qlib

DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

https://github.com/srvCodes/continual-learning-benchmark

Overcoming catastrophic forgetting in neural networks

https://github.com/stijani/elastic-weight-consolidation-tf2

Overcoming catastrophic forgetting in neural networks

https://github.com/synthesio/vision-test

Scalable Deep Learning Logo Detection

https://github.com/thezingaro/multimodal-side-tuning

Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks

https://github.com/tianyu139/tangent-model-composition

Tangent Model Composition for Ensembling and Continual Fine-tuning

https://github.com/torchjd/torchjd

Gradient Episodic Memory for Continual Learning

https://github.com/tueboesen/Active-Learning

Cost-Effective Active Learning for Deep Image Classification

https://github.com/tueboesen/a-optimal-active-learning

Cost-Effective Active Learning for Deep Image Classification

https://github.com/tyler-hayes/embedded-cl

F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning

https://github.com/wang-chen/SubT

Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

https://github.com/wang-chen/interestingness_ros

Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

https://github.com/wang-ml-lab/unified-continual-learning

A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm

https://github.com/wannaa/c-flat

Maintaining Discrimination and Fairness in Class Incremental Learning

https://github.com/wannabeOG/ExpertNet-Pytorch

Learning without Forgetting

https://github.com/wannabeOG/MAS-PyTorch

Overcoming catastrophic forgetting in neural networks

https://github.com/wjmacro/continualmt

Overcoming catastrophic forgetting in neural networks

https://github.com/wutong8023/Lifelong_Relation_Detection_with_Meta_Learning

Sentence Embedding Alignment for Lifelong Relation Extraction

https://github.com/wuzhiyuan2000/FedAgg

Federated Class-Incremental Learning with New-Class Augmented Self-Distillation

https://github.com/wyatuestc/overcoming-catastrophic

Overcoming catastrophic forgetting in neural networks

https://github.com/xduan7/hat-cl

Overcoming catastrophic forgetting in neural networks

https://github.com/xshiguangdaoliux/qlib

DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

https://github.com/xyutao/fscil

Few-Shot Class-Incremental Learning

https://github.com/yhygao/UTNet

Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation

https://github.com/yixinghuang/deepmedicpytorch

A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration

https://github.com/yujiachen-y/StyleBank

StyleBank: An Explicit Representation for Neural Image Style Transfer

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