https://github.com/JiachengLi1995/UCTopic
공식 코드UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining
Papers with Code 아카이브
Contrastive Learning
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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/JiahaoWuGit/DcRec
공식 코드https://github.com/JingbiaoMei/RGCL
공식 코드Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
https://github.com/JoJoNing25/DDAug
공식 코드DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery
https://github.com/JohnGiorgi/DeCLUTR
공식 코드DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
https://github.com/Jonathan-Greif/QBV
공식 코드Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretraining
https://github.com/Jun-jie-Huang/CoCLR
공식 코드CoSQA: 20,000+ Web Queries for Code Search and Question Answering
https://github.com/JunlinHan/CWR
공식 코드https://github.com/JunlinHan/DCLGAN
공식 코드Dual Contrastive Learning for Unsupervised Image-to-Image Translation
https://github.com/JunwenBai/DAPC
공식 코드Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
https://github.com/Junya-Chen/FlatCLR
공식 코드Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
https://github.com/KB-Ding/Multi-stage-Distillaton-Framework
공식 코드Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
https://github.com/KEAML-JLU/Meta-GPS-Plus
공식 코드Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
https://github.com/Kali-Hac/Locality-Awareness-SGE
공식 코드https://github.com/KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection
공식 코드https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast
공식 코드Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast
https://github.com/KeremTurgutlu/clip_art
공식 코드CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
https://github.com/KeremTurgutlu/self_supervised
공식 코드CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
https://github.com/Kim-Minseon/RoCL
공식 코드https://github.com/KishoreP1/DetailCLIP
공식 코드https://github.com/KyleZheng1997/ReSSL
공식 코드ReSSL: Relational Self-Supervised Learning with Weak Augmentation
https://github.com/KyleZheng1997/WCL
공식 코드https://github.com/LIUYUANWEI98/NERTNet
공식 코드Learning Non-target Knowledge for Few-shot Semantic Segmentation
https://github.com/LST1836/BICo
공식 코드Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection
https://github.com/LSY-Cython/CCDM
공식 코드https://github.com/LWHYC/RPR-Loc
공식 코드https://github.com/LabyrinthineLeo/DISCO
공식 코드https://github.com/LanglandsLin/MS2L
공식 코드MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
https://github.com/Lee-Gihun/MixCo-Mixup-Contrast
공식 코드MixCo: Mix-up Contrastive Learning for Visual Representation
https://github.com/LeonResearch/iGCL
공식 코드https://github.com/Levigty/CMCS
공식 코드Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
https://github.com/Levishery/Flywire-Neuron-Tracing
공식 코드Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing
https://github.com/Lili1013/FedPCL_CDR
공식 코드https://github.com/Lin-Yijie/Graph-Matching-Networks/tree/main/COMMON
공식 코드https://github.com/LinguoLi/CrosSCLR
공식 코드3D Human Action Representation Learning via Cross-View Consistency Pursuit
https://github.com/Lireanstar/BPDG
공식 코드Bilateral Personalized Dialogue Generation with Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.