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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/kandorm/CLINE
공식 코드CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding
https://github.com/kangliao929/noise-da
공식 코드Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration
https://github.com/kangningthu/its2clr
공식 코드Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
https://github.com/kanhaoning/Self-supervised-group-meiosis-contrastive-learning-for-EEG-based-emotion-recognition
공식 코드Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition
https://github.com/kanshichao/CBML
공식 코드https://github.com/kaotty/understanding-essl
공식 코드Understanding the Role of Equivariance in Self-supervised Learning
https://github.com/karenyyy/miccai2021_attributegan
공식 코드A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis
https://github.com/kathyliu579/contrastivecross-teachingwithregistration
공식 코드Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
https://github.com/katou2/cstp
공식 코드Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation
https://github.com/keaml-jlu/mi-delight
공식 코드https://github.com/keaml-jlu/simstc
공식 코드A Simple Graph Contrastive Learning Framework for Short Text Classification
https://github.com/keebvin/iars
공식 코드https://github.com/keeganhk/pointvst
공식 코드https://github.com/keio-smilab24/Polos
공식 코드Polos: Multimodal Metric Learning from Human Feedback for Image Captioning
https://github.com/keio-smilab24/relax-former
공식 코드Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive Learning with Dense Labeling
https://github.com/keiradams/chiro
공식 코드Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
https://github.com/kelvin-ywc/lpi
공식 코드Low-rank Prompt Interaction for Continual Vision-Language Retrieval
https://github.com/kennethlimjf/contrastive-learning-in-distilled-models
공식 코드https://github.com/kennysyp/pabot
공식 코드Path and Bone-Contour Regularized Unpaired MRI-to-CT Translation
https://github.com/kesenzhao/dt4rec
공식 코드User Retention-oriented Recommendation with Decision Transformer
https://github.com/keynekassapa13/LAC
공식 코드Self-Supervised Contrastive Learning for Videos using Differentiable Local Alignment
https://github.com/keyu-tian/spark
공식 코드Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
https://github.com/kezheng1204/integer
공식 코드Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
https://github.com/kfallah/manifold-contrastive
공식 코드Manifold Contrastive Learning with Variational Lie Group Operators
https://github.com/kibok90/imix
공식 코드i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
https://github.com/kinux98/SSL_ELN
공식 코드Semi-supervised Semantic Segmentation with Error Localization Network
https://github.com/kit-ps/neuroshield
공식 코드Advancing Brainwave-Based Biometrics: A Large-Scale, Multi-Session Evaluation
https://github.com/knightzjz/ncl-iml
공식 코드https://github.com/knoriy/CLARA
공식 코드CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition
https://github.com/knowledge-computing/geolm
공식 코드GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
https://github.com/kokolerk/divil
공식 코드DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization
https://github.com/kongds/prompt-bert
공식 코드https://github.com/kongds/scaling_sentemb
공식 코드https://github.com/koudounasalkis/CLUES
공식 코드A Contrastive Learning Approach to Mitigate Bias in Speech Models
https://github.com/koukipp/contrastive_transformers_ids
공식 코드Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.