https://github.com/AlexisGuichemerreCode/survey_hist_wsol_sfda
공식 코드Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology
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/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/VLPT-STD
공식 코드Vision-Language Pre-Training for Boosting Scene Text Detectors
https://github.com/Alrash/TimesURL
공식 코드TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning
https://github.com/Angtian/NeMo
공식 코드NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
https://github.com/Armanfard-Lab/C3
공식 코드https://github.com/Asap7772/understanding-rlhf
공식 코드Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
https://github.com/Ascend-Research/GENNAPE
공식 코드GENNAPE: Towards Generalized Neural Architecture Performance Estimators
https://github.com/AslanDing/AutoTCL
공식 코드Parametric Augmentation for Time Series Contrastive Learning
https://github.com/AstraZeneca/NESS
공식 코드https://github.com/BDBC-KG-NLP/AAAI2022_ContrastNet
공식 코드ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
https://github.com/BPYap/SWCL
공식 코드Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images
https://github.com/BUAADreamer/SPN4CIR
공식 코드Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
https://github.com/BestJuly/Inter-intra-video-contrastive-learning
공식 코드Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/BestJuly/Pretext-Contrastive-Learning
공식 코드Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning
https://github.com/BigRedT/info-ground
공식 코드https://github.com/BoChen-Daniel/Expert-Linking
공식 코드CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
https://github.com/Bob-cheng/DepthModelHardening
공식 코드Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
https://github.com/CRIPAC-DIG/GCA
공식 코드https://github.com/CRIPAC-DIG/GRACE
공식 코드https://github.com/CVIR/TCL
공식 코드Semi-Supervised Action Recognition with Temporal Contrastive Learning
https://github.com/CVMI-Lab/SlotCon
공식 코드Self-Supervised Visual Representation Learning with Semantic Grouping
https://github.com/CameronTaylorFL/upl-benchmark
공식 코드Patch-Based Contrastive Learning and Memory Consolidation for Online Unsupervised Continual Learning
https://github.com/Camille112/T-CLAP
공식 코드Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairs
https://github.com/CamilleDelgrange/SSMSRPM
공식 코드A Self-Supervised Model for Multi-modal Stroke Risk Prediction
https://github.com/Capricious-Liu/t-MoCo-v2
공식 코드Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
https://github.com/CasparSwift/CLIM
공식 코드Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization
https://github.com/Chanuku/Semi_ref2sketch_code
공식 코드Semi-supervised reference-based sketch extraction using a contrastive learning framework
https://github.com/ChaoLiang-HUST/ICCL
공식 코드In-context Contrastive Learning for Event Causality Identification
https://github.com/ChaoningZhang/Dual-temperature
공식 코드https://github.com/Chen-Junyang-cn/PLI
공식 코드Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval
https://github.com/ChenLiu-1996/CUTS
공식 코드https://github.com/ChiShengChen/MUSE_EEG
공식 코드Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning
https://github.com/ChintanTrivedi/contrastive-game-representations
공식 코드https://github.com/ChunaraLab/FairDCL-mitigating-urban-rural-disparity
공식 코드Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery
https://github.com/ClaudiaShu/SSL-FER
공식 코드Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
https://github.com/Cloudy1225/NS4GC
공식 코드Reliable Node Similarity Matrix Guided Contrastive Graph Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.