Papers with Code 아카이브
Contrastive Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 4
- 보존된 평가 기록
- 17
Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.
It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.
<span style="color:grey; opacity: 0.6">(Image credit: Schroff et al. 2015)</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Coder-Yu/QRec
공식 코드Socially-Aware Self-Supervised Tri-Training for Recommendation
https://github.com/CupidJay/Scaled-down-self-supervised-learning
공식 코드https://github.com/DAMO-DI-ML/KDD2023-DCdetector
공식 코드DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
https://github.com/DAVEISHAN/TCLR
공식 코드TCLR: Temporal Contrastive Learning for Video Representation
https://github.com/DTennant/CL-Visualizing-Feature-Transformation
공식 코드Improving Contrastive Learning by Visualizing Feature Transformation
https://github.com/DTennant/GPC
공식 코드Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery
https://github.com/DTennant/distill_visual_priors
공식 코드https://github.com/DanielTrosten/mvc
공식 코드Reconsidering Representation Alignment for Multi-view Clustering
https://github.com/Danqi7/584-final
공식 코드https://github.com/DavidZhang73/AssemblyVideoManualAlignment
공식 코드Aligning Step-by-Step Instructional Diagrams to Video Demonstrations
https://github.com/Debatrix/UPCL
공식 코드Rethinking Class-Incremental Learning from a Dynamic Imbalanced Learning Perspective
https://github.com/DeepBCI/Deep-BCI
공식 코드Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition
https://github.com/DeepLearnXMU/CMTMR
공식 코드Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
https://github.com/DeepLearnXMU/User-Driven-NMT
공식 코드https://github.com/DerrickWang005/CRIS.pytorch
공식 코드https://github.com/DianCh/AdaContrast
공식 코드https://github.com/Dongzhijin/MNPWAD
공식 코드Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
https://github.com/Dorniwang/PD-FGC-inference
공식 코드https://github.com/DreamH1gh/TACL2024
공식 코드Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification
https://github.com/Duplums/yAwareContrastiveLearning
공식 코드Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification
https://github.com/Dylan-H-Wang/SLF-RPM
공식 코드https://github.com/ETHRuiGong/TADA
공식 코드https://github.com/EagleW/Multimedia-Generative-Script-Learning-for-Task-Planning
공식 코드https://github.com/EasyFL-AI/EasyFL/tree/master/applications/fedssl
공식 코드Collaborative Unsupervised Visual Representation Learning from Decentralized Data
https://github.com/EliSpectre/CVPR25-AutoSSVH
공식 코드AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing
https://github.com/EmreTaha/TC-time_equivariant_disease_progression
공식 코드https://github.com/EmreTaha/TINC
공식 코드https://github.com/Erican-Ji/CSGDN
공식 코드CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
https://github.com/EvenGu/ExeChecker
공식 코드https://github.com/FaceOnLive/Face-Recognition-SDK-Android
공식 코드Cross-Age Contrastive Learning for Age-Invariant Face Recognition
https://github.com/FangyunWei/SLRT
공식 코드CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
https://github.com/FraCorti/Studying-the-impact-of-magnitude-pruning-on-contrastive-learning-methods
공식 코드Studying the impact of magnitude pruning on contrastive learning methods
https://github.com/Frank-Wang-oss/TS-GAC
공식 코드Graph-Aware Contrasting for Multivariate Time-Series Classification
https://github.com/Frankluox/FewShotCodeBase
공식 코드Boosting Few-Shot Classification with View-Learnable Contrastive Learning
https://github.com/GRAND-Lab/MERIT
공식 코드Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.