https://github.com/gist-ailab/sleepyco
공식 코드SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
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/githubofhyl97/skeattnclr
공식 코드Part Aware Contrastive Learning for Self-Supervised Action Recognition
https://github.com/gklmip/cl-xabsa
공식 코드CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis
https://github.com/gknl/smile
공식 코드SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction
https://github.com/gladia-research-group/cocola
공식 코드COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
https://github.com/gmberton/cosplace
공식 코드Rethinking Visual Geo-localization for Large-Scale Applications
https://github.com/godxuxilie/efficient_acl_via_rcs
공식 코드Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
https://github.com/godxuxilie/enhancing_acl_via_air
공식 코드Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
https://github.com/gongchenghua/self-pro
공식 코드Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
https://github.com/google-deepmind/semppl
공식 코드SemPPL: Predicting pseudo-labels for better contrastive representations
https://github.com/google-research-datasets/ToTTo
공식 코드https://github.com/google-research/big_vision
공식 코드https://github.com/google-research/compressive-visual-representations
공식 코드https://github.com/google-research/deep_representation_one_class
공식 코드Learning and Evaluating Representations for Deep One-class Classification
https://github.com/google-research/fnc
공식 코드Boosting Contrastive Self-Supervised Learning with False Negative Cancellation
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/cola
공식 코드Contrastive Learning of General-Purpose Audio Representations
https://github.com/google-research/google-research/tree/master/fvlm/dito
공식 코드Region-centric Image-Language Pretraining for Open-Vocabulary Detection
https://github.com/google-research/google-research/tree/master/fvlm/rovit
공식 코드Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers
https://github.com/google-research/google-research/tree/master/supcon
공식 코드https://github.com/google-research/scenic
공식 코드Location-Aware Self-Supervised Transformers for Semantic Segmentation
https://github.com/google-research/scenic/tree/main/scenic/projects/verbs_in_action
공식 코드Verbs in Action: Improving verb understanding in video-language models
https://github.com/google-research/simclr
공식 코드A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/google-research/syn-rep-learn
공식 코드StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
https://github.com/google-research/xmcgan_image_generation
공식 코드Cross-Modal Contrastive Learning for Text-to-Image Generation
https://github.com/gowitheflow-1998/la-ser-cubed
공식 코드Length is a Curse and a Blessing for Document-level Semantics
https://github.com/gowitheflow-1998/on-isotropy-of-contrastive-srl
공식 코드https://github.com/gqq1210/psp
공식 코드PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
https://github.com/grand-lab/sublime
공식 코드https://github.com/grasp-lyrl/picture_of_space_of_tasks
공식 코드https://github.com/gridai-labs/aavae
공식 코드https://github.com/grip-unina/poi-forensics
공식 코드https://github.com/grizzledtroubadour/ProtFAD
공식 코드ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction
https://github.com/gsidiropoulos/dense-retrieval-against-misspellings
공식 코드Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings
https://github.com/gsidiropoulos/typo-robust-multi-positive-dr
공식 코드Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive Learning
https://github.com/guangjinpan/lwlm
공식 코드Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.