https://github.com/simomagi/elastic_feature_consolidation
공식 코드Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning
Papers with Code 아카이브
class-incremental learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/skylorh/FedCLASS
공식 코드Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
https://github.com/srebuffi/iCaRL
공식 코드https://github.com/stonemo/cign
공식 코드Class-Incremental Grouping Network for Continual Audio-Visual Learning
https://github.com/sud0301/essentials_for_CIL
공식 코드https://github.com/sun-hailong/aaai25-mos
공식 코드MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning
https://github.com/sun-hailong/cvpr24-ease
공식 코드Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning
https://github.com/sun-hailong/lamda-pilot
공식 코드https://github.com/tachyonicclock/surprisenet-cikm-23
공식 코드Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning
https://github.com/tamasham/napa-vq
공식 코드NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning
https://github.com/tambourine666/Covariance-Space-Regularize
공식 코드Covariance-based Space Regularization for Few-shot Class Incremental Learning
https://github.com/tdemin16/multi-lane
공식 코드Less is more: Summarizing Patch Tokens for efficient Multi-Label Class-Incremental Learning
https://github.com/tf63/acmap
공식 코드Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning
https://github.com/thunderbeee/zscl
공식 코드Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models
https://github.com/tiansongs/pl-fscil
공식 코드PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental Learning
https://github.com/tldoan/clip-m3
공식 코드A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
https://github.com/tldoan/clip_m3
공식 코드A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
https://github.com/tmtuan1307/lander
공식 코드Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
https://github.com/townim-faisal/fscil-3d
공식 코드Few-shot Class-incremental Learning for 3D Point Cloud Objects
https://github.com/tulerfeng/OTGNet
공식 코드https://github.com/tyler-hayes/Deep_SLDA
공식 코드Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis
https://github.com/ubc-tea/continual-impression-ccsi
공식 코드CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning
https://github.com/umbertocappellazzo/CL_SLU
공식 코드https://github.com/vinuni-vishc/cg-imif
공식 코드Multi-stream Fusion for Class Incremental Learning in Pill Image Classification
https://github.com/vita-qzh/apart
공식 코드Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning
https://github.com/vt-nlp/ice
공식 코드https://github.com/wangkiw/iclr23-memo
공식 코드A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
https://github.com/wangkiw/teen
공식 코드Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
https://github.com/weiguopian/av-cil_iccv2023
공식 코드https://github.com/witdsl/krt-mlcil
공식 코드Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
https://github.com/wonseok1017/nn-rfr
공식 코드https://github.com/xfflzl/cim-cil
공식 코드Class-Incremental Exemplar Compression for Class-Incremental Learning
https://github.com/xialeiliu/GFR-IL
공식 코드https://github.com/xialeiliu/long-tailed-cil
공식 코드https://github.com/xiaojieli0903/ckpd-fscil
공식 코드Continuous Knowledge-Preserving Decomposition for Few-Shot Continual Learning
https://github.com/xiaojieli0903/cpkd_fscil
공식 코드Continuous Knowledge-Preserving Decomposition for Few-Shot Continual Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.