Papers with Code 아카이브
Contrastive Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 4
- 보존된 평가 기록
- 17
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/andyjzhao/wsdm23-gsr
공식 코드Self-Supervised Graph Structure Refinement for Graph Neural Networks
https://github.com/andypinxinliu/HumanFaceProject/tree/main/assets/GaussianStyle
공식 코드https://github.com/angel-1999/mcore
공식 코드Multi-Stage Contrastive Regression for Action Quality Assessment
https://github.com/angeloucn/min_max_similarity
공식 코드https://github.com/angrycai/sscc
공식 코드Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
https://github.com/anhduy0911/coinception
공식 코드Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder
https://github.com/anhtunguyen98/bislu
공식 코드https://github.com/anna-vanelst/simclr-pb
공식 코드Tight PAC-Bayesian Risk Certificates for Contrastive Learning
https://github.com/annbless/regioncl
공식 코드RegionCL: Can Simple Region Swapping Contribute to Contrastive Learning?
https://github.com/annonymoussubmission/explain-cl
공식 코드https://github.com/anok3111/loco
공식 코드Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation
https://github.com/anok3111/sada
공식 코드Stain-aware Domain Alignment for Imbalance Blood Cell Classification
https://github.com/anonymity0403/mmcl
공식 코드https://github.com/anonymous-author-sub/seeable
공식 코드SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
https://github.com/anshulbshah/MMCL
공식 코드https://github.com/anshulbshah/halp
공식 코드HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
https://github.com/answermycode/ebrec
공식 코드Enhancing Item-level Bundle Representation for Bundle Recommendation
https://github.com/anthonymartin/RKDO-recursive-kl-divergence-optimization
공식 코드Recursive KL Divergence Optimization: A Dynamic Framework for Representation Learning
https://github.com/antoalli/Deco
공식 코드https://github.com/antofuller/croma
공식 코드CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
https://github.com/aoluming/IDKG
공식 코드https://github.com/aone-nlp/fsre-sacon
공식 코드Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction
https://github.com/apple/corenet
공식 코드https://github.com/applied-machine-learning-lab/dt4ier
공식 코드Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
https://github.com/aptx1231/start
공식 코드Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
https://github.com/archiplab-linfengzhang/contrastive-deep-supervision
공식 코드https://github.com/arghosh/noisy_label_pretrain
공식 코드Contrastive Learning Improves Model Robustness Under Label Noise
https://github.com/ariG23498/G-SimCLR
공식 코드G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling
https://github.com/aristotelisballas/listen2yourheart
공식 코드https://github.com/aristotelisballas/m2cl
공식 코드Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization
https://github.com/arplaboratory/vg_ssl
공식 코드VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization
https://github.com/arvindmvepa/al-seg
공식 코드Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation
https://github.com/ashwinipokle/contrastive_landscape
공식 코드Contrasting the landscape of contrastive and non-contrastive learning
https://github.com/askrbayern/neuro2voc_thesis
공식 코드https://github.com/asrafulashiq/transfer_broad
공식 코드A Broad Study on the Transferability of Visual Representations with Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.