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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/yrbobo/mqs-ecl
공식 코드Medical Question Summarization with Entity-driven Contrastive Learning
https://github.com/yrcong/learning_similarity_between_graphs_images
공식 코드SPAN: Learning Similarity between Scene Graphs and Images with Transformers
https://github.com/yu-fangxu/eacl
공식 코드Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
https://github.com/yu-gi-oh-leilei/sadcl
공식 코드Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification
https://github.com/yu-gi-oh-leilei/trm-ml
공식 코드Text-Region Matching for Multi-Label Image Recognition with Missing Labels
https://github.com/yuangongnd/cav-mae
공식 코드https://github.com/yubofeng2023/promptcl
공식 코드PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning
https://github.com/yueeeeeeee/self-supervised-qa
공식 코드QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
https://github.com/yueliu1999/awesome-deep-group-recommendation
공식 코드End-to-end Learnable Clustering for Intent Learning in Recommendation
https://github.com/yueliu1999/elcrec
공식 코드End-to-end Learnable Clustering for Intent Learning in Recommendation
https://github.com/yuemingjin/stswincl
공식 코드Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
https://github.com/yuh-yang/kgcl-sigir22
공식 코드https://github.com/yuhaozhang/convirt
공식 코드Contrastive Learning of Medical Visual Representations from Paired Images and Text
https://github.com/yuhui-zh15/drml
공식 코드https://github.com/yujiaoshi/g2sweakly
공식 코드Weakly-supervised Camera Localization by Ground-to-satellite Image Registration
https://github.com/yujieouo/pstl
공식 코드Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
https://github.com/yukilulu/cac
공식 코드Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation
https://github.com/yukimasano/self-label
공식 코드Self-labelling via simultaneous clustering and representation learning
https://github.com/yukui-1999/ecg-cmr
공식 코드https://github.com/yul-git/glocalclip
공식 코드GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection
https://github.com/yuleiqin/fopro
공식 코드FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning
https://github.com/yunan-wang33/sdml
공식 코드Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learning
https://github.com/yuncheng97/ecc-polypdet
공식 코드ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection
https://github.com/yunguo224/NLCL
공식 코드Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity
https://github.com/yunhanwang1105/GazeTech
공식 코드Investigation of Architectures and Receptive Fields for Appearance-based Gaze Estimation
https://github.com/yunhaoyang234/patch-subspace-learning-autoencoder
공식 코드Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing
https://github.com/yunjinli/SADG-SegmentAnyDynamicGaussian
공식 코드https://github.com/yunyikristy/CM-ACC
공식 코드Active Contrastive Learning of Audio-Visual Video Representations
https://github.com/yunyikristy/global_local
공식 코드https://github.com/yupei-wang/uninl
공식 코드https://github.com/yuqinghuang01/mmcl-tabular-fundus
공식 코드Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning
https://github.com/yuqinie98/patchtst
공식 코드https://github.com/yutanagano/tcrlm
공식 코드https://github.com/yutinghe-list/cover
공식 코드Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
https://github.com/yuwang-1024/contrastvae
공식 코드ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.