https://github.com/lanxiang1017/dynamicbadpairmining_iclr24
공식 코드Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
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/laohur/gur
공식 코드https://github.com/laowangzi/cikgrec
공식 코드Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models
https://github.com/laura-wang/video-pace
공식 코드Self-supervised Video Representation Learning by Pace Prediction
https://github.com/lavine-lmu/bi-acl
공식 코드Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation
https://github.com/layer6ai-labs/tabular-class-conditioned-ssl
공식 코드Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation
https://github.com/lblaoke/gCooL
공식 코드https://github.com/lccc0528/stable
공식 코드StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation
https://github.com/lccurious/MuLTI
공식 코드https://github.com/learninginvision/codegan
공식 코드CoDeGAN: Contrastive Disentanglement for Generative Adversarial Network
https://github.com/leechungpa/embedding-similarity-cl
공식 코드https://github.com/leeyejin1231/amplehate
공식 코드AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection
https://github.com/lehaifeng/T-GCN
공식 코드Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations
https://github.com/lehengthu/advinfonce
공식 코드Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
https://github.com/lehengthu/alpharec
공식 코드Language Representations Can be What Recommenders Need: Findings and Potentials
https://github.com/leileigan/ContrastiveLJP
공식 코드Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
https://github.com/leiluk1/codesearcher
공식 코드https://github.com/leiwangr/cl
공식 코드https://github.com/lemei/unimse
공식 코드UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
https://github.com/lemonsis/ctrlstruct
공식 코드CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation
https://github.com/leo02016/fairmvc
공식 코드https://github.com/leo02016/herocon
공식 코드https://github.com/leomlck/medimp
공식 코드MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation
https://github.com/leonidprinceton/oxels
공식 코드Aligning Motion-Blurred Images Using Contrastive Learning on Overcomplete Pixels
https://github.com/leooo-shen/tvrl
공식 코드Spatiotemporal Representation Learning for Short and Long Medical Image Time Series
https://github.com/leosampaio/scene-designer
공식 코드Scene Designer: a Unified Model for Scene Search and Synthesis from Sketch
https://github.com/leungtsang/cpcdr
공식 코드Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation
https://github.com/levigty/acl
공식 코드Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
https://github.com/levigty/aimclr
공식 코드https://github.com/lewis081/ccl-net
공식 코드Underwater Image Enhancement with Cascaded Contrastive Learning
https://github.com/lezhang7/Enhance-FineGrained
공식 코드https://github.com/lhao499/HDGE
공식 코드Hybrid Discriminative-Generative Training via Contrastive Learning
https://github.com/lhryang/crl_egpg
공식 코드Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation
https://github.com/lhtie/bio-domain-transfer
공식 코드Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences
https://github.com/liamchalcroft/contrast-squared
공식 코드Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
https://github.com/liang-ntu/techinical_report_remvc
공식 코드Region Embedding with Intra and Inter-View Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.