https://github.com/bobzwr/cross-reconstruction-transformer
공식 코드Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer
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/boese0601/dyadic-interaction-modeling
공식 코드https://github.com/boschresearch/expclr
공식 코드Utilizing Expert Features for Contrastive Learning of Time-Series Representations
https://github.com/boschresearch/rince
공식 코드Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives
https://github.com/boschresearch/what-matters-for-meta-learning
공식 코드https://github.com/bowang-lab/bleep
공식 코드https://github.com/bowang-lab/ecg-fm
공식 코드https://github.com/bowang-rw-02/scl-one-for-four
공식 코드One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning
https://github.com/bowen-upenn/scene_graph_commonsense
공식 코드Hierarchical Relationships: A New Perspective to Enhance Scene Graph Generation
https://github.com/brandonhanx/mmf
공식 코드FashionViL: Fashion-Focused Vision-and-Language Representation Learning
https://github.com/brandonhanx/textreid
공식 코드https://github.com/brdav/cma
공식 코드Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation
https://github.com/brendel-group/cl-ica
공식 코드https://github.com/brgcl-code/brgcl-code
공식 코드https://github.com/brian7685/Multimodal-Clustering-Network
공식 코드Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
https://github.com/brianlsy98/dice
공식 코드DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining
https://github.com/brightjade/SimCKP
공식 코드SimCKP: Simple Contrastive Learning of Keyphrase Representations
https://github.com/broadinstitute/ml4h
공식 코드Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling
https://github.com/btzyd/jointcrop
공식 코드Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"
https://github.com/buaadreamer/ccrk
공식 코드Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
https://github.com/budzianowski/multiwoz
공식 코드https://github.com/bupt-ai-cz/hhcl-reid
공식 코드Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
https://github.com/bupt-gamma/gcil
공식 코드Graph Contrastive Invariant Learning from the Causal Perspective
https://github.com/buptsjzhang/std-cl
공식 코드Spatial-Temporal Decoupling Contrastive Learning for Skeleton-based Human Action Recognition
https://github.com/bvanberl/ivpp
공식 코드Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound
https://github.com/bych-lee/gsg
공식 코드Implicit Contrastive Representation Learning with Guided Stop-gradient
https://github.com/byronji/cellclat
공식 코드https://github.com/byronji/drgcl
공식 코드Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
https://github.com/bzhanguru/paper_class-m
공식 코드https://github.com/c-liangyu/csval
공식 코드Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
https://github.com/cai-jianfeng/HCLNet
공식 코드Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification
https://github.com/caijd2000/MICCAI2023-VVC-Screening
공식 코드Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening
https://github.com/caiqi/Joint-Contrastive-Learning
공식 코드https://github.com/caixdlab/cl4capro
공식 코드Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values
https://github.com/calubkk/clha
공식 코드CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
https://github.com/calvin-zcx/scehr
공식 코드SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.