https://github.com/xeaver/emotionclip
공식 코드Learning Emotion Representations from Verbal and Nonverbal Communication
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/xfactlab/i0t
공식 코드I0T: Embedding Standardization Method Towards Zero Modality Gap
https://github.com/xhu248/semi_cotrast_seg
공식 코드Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
https://github.com/xiaRunZe/Memory-Disentangling
공식 코드Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information
https://github.com/xiami2019/claif
공식 코드Improving Contrastive Learning of Sentence Embeddings from AI Feedback
https://github.com/xiangxu-0103/superflow
공식 코드4D Contrastive Superflows are Dense 3D Representation Learners
https://github.com/xiao7199/structural-adversarial-objectives
공식 코드Structural Adversarial Objectives for Self-Supervised Representation Learning
https://github.com/xiaohao-liu/clhe
공식 코드Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
https://github.com/xiaojieli0903/genview
공식 코드GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning
https://github.com/xiaolisean/cognitive-map
공식 코드SeanNet: Semantic Understanding Network for Localization Under Object Dynamics
https://github.com/xiaomoguhz/ov-dquo
공식 코드https://github.com/xiaxin1998/cotrec
공식 코드Self-Supervised Graph Co-Training for Session-based Recommendation
https://github.com/xieenze/DetCo
공식 코드DetCo: Unsupervised Contrastive Learning for Object Detection
https://github.com/xieh97/retrieval-relevance-crowdsourcing
공식 코드Crowdsourcing and Evaluating Text-Based Audio Retrieval Relevances
https://github.com/xieyc99/dov4cl
공식 코드Dataset Ownership Verification in Contrastive Pre-trained Models
https://github.com/xihongyang1999/ccgc
공식 코드https://github.com/xihongyang1999/convert
공식 코드CONVERT:Contrastive Graph Clustering with Reliable Augmentation
https://github.com/xihongyang1999/graphlearner
공식 코드GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
https://github.com/xilin1991/CluterNet
공식 코드Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
https://github.com/xinghaow99/denosent
공식 코드DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
https://github.com/xinleihe/contrastiveleaks
공식 코드Quantifying and Mitigating Privacy Risks of Contrastive Learning
https://github.com/xinrui17/bncl
공식 코드Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
https://github.com/xixianyong/musecl
공식 코드MuseCL: Predicting Urban Socioeconomic Indicators via Multi-Semantic Contrastive Learning
https://github.com/xiyanghu/multilingualir
공식 코드Language Agnostic Multilingual Information Retrieval with Contrastive Learning
https://github.com/xiyuanzh/unimts
공식 코드https://github.com/xjaw/s4rec
공식 코드Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
https://github.com/xjtuyw/pnp
공식 코드Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discovery
https://github.com/xlearning-scu/2023-iccv-common
공식 코드https://github.com/xljh0520/jotr
공식 코드JOTR: 3D Joint Contrastive Learning with Transformers for Occluded Human Mesh Recovery
https://github.com/xlwang233/calliper
공식 코드Multimodal Contrastive Learning of Urban Space Representations from POI Data
https://github.com/xlwang233/into-the-unknown
공식 코드https://github.com/xmed-lab/distillingself
공식 코드Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions
https://github.com/xmudm/sentiwsp
공식 코드Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
https://github.com/xpq-tech/code-llmedit
공식 코드https://github.com/xrenaa/DisCo
공식 코드https://github.com/xsgxlz/how-does-contrastive-learning-organize-images
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.