https://github.com/LiuShiBen/DAFC
공식 코드Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SunWenJu123/DCPOC
공식 코드Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
https://github.com/Tsebeb/cvww_cir_horde
공식 코드Incremental Learning with Repetition via Pseudo-Feature Projection
https://github.com/ZHUANGHP/Analytic-continual-learning
공식 코드https://github.com/alviur/ldc
공식 코드Exemplar-free Continual Representation Learning via Learnable Drift Compensation
https://github.com/alviur/pocon_wacv2024
공식 코드Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
https://github.com/chaoqi7/bsa-cil-3d
공식 코드https://github.com/chen-yizhu/gacl
공식 코드https://github.com/dipamgoswami/adc
공식 코드Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning
https://github.com/dipamgoswami/fecam
공식 코드FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
https://github.com/drimpossible/randumb
공식 코드Random Representations Outperform Online Continually Learned Representations
https://github.com/fang-d/air
공식 코드https://github.com/fszatkowski/cl-teacher-adaptation
공식 코드Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
https://github.com/g-u-n/pycil
공식 코드S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
https://github.com/gmum/icicle
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/seed
공식 코드Divide and not forget: Ensemble of selectively trained experts in Continual Learning
https://github.com/iamwangyabin/s-prompts
공식 코드S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
https://github.com/kikihub/consense
공식 코드ConSense: Continually Sensing Human Activity with WiFi via Growing and Picking
https://github.com/liuyuchen-cz/f-oal
공식 코드https://github.com/ocram17/gcab-cfdc
공식 코드https://github.com/pauljanson002/pretrained-cl
공식 코드A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning
https://github.com/rhe502/icml25-dpcr
공식 코드https://github.com/scarlet0703/LoRA-Sub-DRS
공식 코드LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning
https://github.com/scok30/tass
공식 코드Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning
https://github.com/simomagi/elastic_feature_consolidation
공식 코드Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning
https://github.com/sinhasaptarshi/EveryShotCounts
공식 코드Every Shot Counts: Using Exemplars for Repetition Counting in Videos
https://github.com/srvCodes/continual_learning_with_vit
공식 코드https://github.com/stapaw/stp
공식 코드Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual Learning
https://github.com/tf63/acmap
공식 코드Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning
https://github.com/tom-tym/apg
공식 코드When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
https://github.com/vita-qzh/apart
공식 코드Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning
https://github.com/yuyangdu01/llm-cl-vqa
공식 코드LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery
https://github.com/zhoujiahuan1991/aaai2025-lreid-dask
공식 코드https://github.com/zj-jayzhang/Federated-Class-Continual-Learning
공식 코드TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.