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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/disrec/disrec
공식 코드Disentangled Modeling of Preferences and Social Influence for Group Recommendation
https://github.com/djz233/clusterns
공식 코드Clustering-Aware Negative Sampling for Unsupervised Sentence Representation
https://github.com/dk-liang/uniseg3d
공식 코드https://github.com/dl4mhealth/comet
공식 코드Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
https://github.com/dl4mhealth/ts-contrastive-augmentation-recommendation
공식 코드Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification
https://github.com/dlcjfgmlnasa/NeuroNet
공식 코드https://github.com/dlcjfgmlnasa/SynthSleepNet
공식 코드https://github.com/dll-wu/is-cse
공식 코드Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
https://github.com/dllllb/coles-paper
공식 코드CoLES: Contrastive Learning for Event Sequences with Self-Supervision
https://github.com/dm4m/fsru
공식 코드https://github.com/dmis-lab/molpla
공식 코드MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
https://github.com/dmis-lab/tour
공식 코드Optimizing Test-Time Query Representations for Dense Retrieval
https://github.com/dmitrav/morpho-learner
공식 코드https://github.com/dnap512/SelfReg
공식 코드SelfReg: Self-supervised Contrastive Regularization for Domain Generalization
https://github.com/doc-doc/covgt
공식 코드Contrastive Video Question Answering via Video Graph Transformer
https://github.com/dongguanting/msdp-fewshot-ner
공식 코드A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
https://github.com/donghao51/simmmdg
공식 코드SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
https://github.com/dongkikim95/d-sla
공식 코드Graph Self-supervised Learning with Accurate Discrepancy Learning
https://github.com/dongkwani/upcsc
공식 코드Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization
https://github.com/dongqi-fan/irconstyle
공식 코드IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer
https://github.com/dongruihust/cl4cvr
공식 코드https://github.com/dongwei156/n2n
공식 코드Self-Supervised Node Representation Learning via Node-to-Neighbourhood Alignment
https://github.com/dongxiangjue/closed-book-question-generation-via-contrastive-learning
공식 코드https://github.com/dopaminelcy/alta
공식 코드Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models
https://github.com/downeykking/l2cl
공식 코드L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
https://github.com/dr-bell/crossl
공식 코드CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
https://github.com/dravyanshsharma/adversarial-contrastive
공식 코드https://github.com/dreammr/pcl
공식 코드Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
https://github.com/drluo/rtm
공식 코드Toward Real Text Manipulation Detection: New Dataset and New Solution
https://github.com/dsam99/pc_clip
공식 코드https://github.com/dsgt-kaggle-clef/geolifeclef-2024
공식 코드Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024
https://github.com/du-team/fcacc
공식 코드https://github.com/duplums/contrastive-decoupled-uniformity
공식 코드Integrating Prior Knowledge in Contrastive Learning with Kernel
https://github.com/duyhominhnguyen/LVM-Med
공식 코드https://github.com/duyngtr16061999/kdmcse
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.