https://github.com/lithium770/unsupervised-person-re-id-with-stochastic-training-strategy
공식 코드Unsupervised Person Re-identification with Stochastic Training Strategy
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/litong99/citss
공식 코드https://github.com/little1tow/DELE_pytorch
공식 코드Description-Enhanced Label Embedding Contrastive Learning for Text Classification
https://github.com/liu-xb/GCMT
공식 코드Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification
https://github.com/liubin06/BCL
공식 코드https://github.com/liubin06/variationalbpr
공식 코드https://github.com/liujf69/MMCL-Action
공식 코드Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
https://github.com/liujmzzz/openncd
공식 코드https://github.com/liulab1356/CL-ImpPreNet
공식 코드https://github.com/liun-online/HeCo
공식 코드Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning
https://github.com/liun-online/spco
공식 코드Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
https://github.com/liunian-jay/icmvc
공식 코드Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding
https://github.com/liuquan98/gcl
공식 코드Density-invariant Features for Distant Point Cloud Registration
https://github.com/liushunyu/cia
공식 코드Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
https://github.com/liuweide01/GQNet-Few-shot-segmentation
공식 코드Few-Shot Segmentation with Global and Local Contrastive Learning
https://github.com/liuxin0824/comsd
공식 코드ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery
https://github.com/liuxu77/stgcl
공식 코드When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
https://github.com/liuyixin-louis/diffshortcut
공식 코드Rethinking and Defending Protective Perturbation in Personalized Diffusion Models
https://github.com/liuziyu77/rar
공식 코드RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
https://github.com/lividwo/ki
공식 코드Lexical Knowledge Internalization for Neural Dialog Generation
https://github.com/liweiyangv/dpcl
공식 코드https://github.com/liyihao76/MMAC_LaTIM_Solution
공식 코드https://github.com/liyongqi2002/TadNER
공식 코드Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
https://github.com/lizhaoliu-lec/cg-vlm
공식 코드Contrastive Vision-Language Alignment Makes Efficient Instruction Learner
https://github.com/lizheren/MSVCL_MICCAI2021
공식 코드Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
https://github.com/lizishi/detrc
공식 코드https://github.com/ljjcoder/PCL
공식 코드https://github.com/ljjcoder/ehtdi
공식 코드https://github.com/ljjcoder/probabilistic-contrastive-learning
공식 코드https://github.com/lliangchenc/DeepChannel
공식 코드DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
https://github.com/llm-lab-org/CLASP
공식 코드CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval
https://github.com/llmir/yolocurvseg
공식 코드YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation
https://github.com/longbai1006/surgical-vqlaplus
공식 코드https://github.com/loralinh/semi-supervised-crowd-counting-via-density-agency
공식 코드https://github.com/lorenmt/reco
공식 코드https://github.com/love481/Cross-modal-Contrastive-Learning-with-Asymmetric-Co-attention-Network-for-Video-Moment-Retrieval
공식 코드Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.