https://github.com/ppwwyyxx/moco.tensorflow
공식 코드Momentum Contrast for Unsupervised Visual Representation Learning
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/prajwalsingh/eegstylegan-ada
공식 코드Learning Robust Deep Visual Representations from EEG Brain Recordings
https://github.com/prakashchhipa/learning-self-supervised-representations-for-label-efficient-cross-domain-knowledge-transfer-on-drf
공식 코드https://github.com/prakashchhipa/robsutness-evaluation-of-self-supervised-methods-distribution-shifts-and-corruptions
공식 코드Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?
https://github.com/premiertaco/premier-taco
공식 코드https://github.com/princeton-nlp/SimCSE
공식 코드https://github.com/princeton-nlp/mabel
공식 코드MABEL: Attenuating Gender Bias using Textual Entailment Data
https://github.com/princeton-nlp/semsup-xc
공식 코드SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
https://github.com/pris-cv/caption-feature-space-regularization
공식 코드https://github.com/project-lighter/lighter
공식 코드https://github.com/prowontheus/isd-mae
공식 코드Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT Segmentation
https://github.com/pseshadri9/contrastive-music-performance-assessment
공식 코드Improving Music Performance Assessment with Contrastive Learning
https://github.com/pseudo-skye/triad
공식 코드Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
https://github.com/psunlpgroup/container
공식 코드CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning
https://github.com/psunlpgroup/famesumm
공식 코드FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
https://github.com/puerrrr/focal-infonce
공식 코드Improving Contrastive Learning of Sentence Embeddings with Focal-InfoNCE
https://github.com/pujacomputes/datapropsgraphssl
공식 코드Analyzing Data-Centric Properties for Graph Contrastive Learning
https://github.com/pushkarjajoria/Text-Conditioned-Drumbeat-Generation
공식 코드Text Conditioned Symbolic Drumbeat Generation using Latent Diffusion Models
https://github.com/pvop/clh3g
공식 코드Contrastive Learning enhanced Author-Style Headline Generation
https://github.com/pwsong-ustc/ida
공식 코드Intermediate Domain-guided Adaptation for Unsupervised Chorioallantoic Membrane Vessel Segmentation
https://github.com/pyg-team/pytorch_geometric
공식 코드What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
https://github.com/q-cardia/ssp-cmr-cine-segmentation
공식 코드Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine Segmentation
https://github.com/qcraftai/cross-modal-ssl
공식 코드Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
https://github.com/qianduoduolr/Spa-then-Temp
공식 코드Spatial-then-Temporal Self-Supervised Learning for Video Correspondence
https://github.com/qing145/caged
공식 코드https://github.com/qingchen239/ld-detr
공식 코드LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection
https://github.com/qingwang-isu/augure
공식 코드Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
https://github.com/qinhsiu/icsrec
공식 코드Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
https://github.com/qinhsiu/mclrec
공식 코드Meta-optimized Contrastive Learning for Sequential Recommendation
https://github.com/qiukunpeng/dscl
공식 코드Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image Classification
https://github.com/qiuzx2/CIBHash
공식 코드Unsupervised Hashing with Contrastive Information Bottleneck
https://github.com/qiyuw/peercl
공식 코드PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
https://github.com/qizhuang-qz/fedcspc
공식 코드Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
https://github.com/qizhust/cmcl_vqa_pl
공식 코드Cross-Modal Contrastive Learning for Robust Reasoning in VQA
https://github.com/qjh1996/hutd
공식 코드https://github.com/qqqyd/visu
공식 코드Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.