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class-incremental learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/erummushtaq/cromo-mixup
공식 코드CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
https://github.com/facebookresearch/agem
공식 코드https://github.com/fang-d/air
공식 코드https://github.com/ferdinandzju/iap
공식 코드IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting
https://github.com/feyzaakyurek/subspace-reg
공식 코드Subspace Regularizers for Few-Shot Class Incremental Learning
https://github.com/firatozdemir/LwfSeg-AeiSeg
공식 코드Extending Pretrained Segmentation Networks with Additional Anatomical Structures
https://github.com/fischer-tom/inemo
공식 코드iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
https://github.com/fszatkowski/cl-teacher-adaptation
공식 코드Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
https://github.com/ftraeuble/experiments_discrete_key_value_bottleneck
공식 코드https://github.com/fwu11/macil
공식 코드Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning
https://github.com/g-u-n/pycil
공식 코드https://github.com/gawainxu/openincremen
공식 코드OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
https://github.com/ghy0501/pilora
공식 코드PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning
https://github.com/gmum/icicle
공식 코드https://github.com/google-research/l2p
공식 코드https://github.com/gregoirepetit/fetril
공식 코드FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning
https://github.com/grypesc/adagauss
공식 코드Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning
https://github.com/grypesc/camp
공식 코드Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
https://github.com/grypesc/seed
공식 코드Divide and not forget: Ensemble of selectively trained experts in Continual Learning
https://github.com/gydpku/gsa
공식 코드Dealing with Cross-Task Class Discrimination in Online Continual Learning
https://github.com/habibslim/dkt-for-cil
공식 코드Dataset Knowledge Transfer for Class-Incremental Learning without Memory
https://github.com/haiv-lab/ssiat
공식 코드Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer
https://github.com/hamedhemati/cir
공식 코드https://github.com/haoranchen/promptfusion
공식 코드PromptFusion: Decoupling Stability and Plasticity for Continual Learning
https://github.com/hit-leaderone/filp-3d
공식 코드FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models
https://github.com/hit-leaderone/flip-3d
공식 코드FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models
https://github.com/hongsinlee/flair
공식 코드Enhancing Robustness in Incremental Learning with Adversarial Training
https://github.com/hy2mk/cgcd
공식 코드Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery
https://github.com/ibm/constrained-fscil
공식 코드https://github.com/ifnspaml/CIL_Segmentation
공식 코드Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels
https://github.com/ihaeyong/pfnr
공식 코드Continual Learning: Forget-free Winning Subnetworks for Video Representations
https://github.com/ihaeyong/soft-tf
공식 코드https://github.com/ihaeyong/softnet-fscil
공식 코드On the Soft-Subnetwork for Few-shot Class Incremental Learning
https://github.com/isee-laboratory/revisting_fscil
공식 코드Rethinking Few-shot Class-incremental Learning: Learning from Yourself
https://github.com/ivsucram/cfa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.