https://github.com/judydnguyen/pardon-feddg
공식 코드PARDON: Privacy-Aware and Robust Federated Domain Generalization
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/jukiecheung/mvitime
공식 코드Contrastive Learning for Sleep Staging based on Inter Subject Correlation
https://github.com/julian075/catalog
공식 코드CATALOG: A Camera Trap Language-guided Contrastive Learning Model
https://github.com/juliendenize/eztorch
공식 코드Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning
https://github.com/junl21/lacl
공식 코드Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis
https://github.com/junlinhan/cropmix
공식 코드CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
https://github.com/junpliu/condigsum
공식 코드Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
https://github.com/juntae9926/rdcd
공식 코드Relational Self-supervised Distillation with Compact Descriptors for Image Copy Detection
https://github.com/junwenbai/c-gmvae
공식 코드Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
https://github.com/junwoopark92/self-supervised-contrastive-forecsating
공식 코드Self-Supervised Contrastive Learning for Long-term Forecasting
https://github.com/junxia97/progcl
공식 코드ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
https://github.com/junxia97/simgrace
공식 코드SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
https://github.com/justarter/flip
공식 코드https://github.com/justin941208/mapconnet
공식 코드MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning
https://github.com/justinas256/2d-mouse-brain-identification
공식 코드https://github.com/justsmart/DICNet
공식 코드https://github.com/justsmart/DICNet-mindspore
공식 코드https://github.com/justus-jonas/imaginarynlp
공식 코드https://github.com/juszzi/pen
공식 코드https://github.com/jwliu-cc/svg
공식 코드Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation
https://github.com/jwoo5/fairseq-signals
공식 코드https://github.com/jxhuang0508/CaCo
공식 코드Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
https://github.com/jyf123/html
공식 코드Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
https://github.com/jz48/regexplainer
공식 코드RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
https://github.com/k-stacke/ssl-pathology
공식 코드Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
https://github.com/kadarsh22/Sebra
공식 코드https://github.com/kaen2891/stethoscope-guided_supervised_contrastive_learning
공식 코드https://github.com/kahimwong/fontguard
공식 코드FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge
https://github.com/kaike-zhang/dyted
공식 코드DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph
https://github.com/kaiwenzha/contrastive-poisoning
공식 코드Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
https://github.com/kaize0409/s-3-cl
공식 코드Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning
https://github.com/kakaobrain/scrl
공식 코드https://github.com/kakaobrain/tcl
공식 코드https://github.com/kali-hac/hi-mpc
공식 코드https://github.com/kali-hac/simmc
공식 코드https://github.com/kali-hac/spc-mgr
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.