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Contrastive Learning

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.

(Image credit: Schroff et al. 2015)

Papers

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Showing 4951–5000 of 6661 papers

TitleStatusHype
Bayesian Self-Supervised Contrastive LearningCode0
Leveraging the Third Dimension in Contrastive Learning—0
Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks—0
Understanding Self-Supervised Pretraining with Part-Aware Representation LearningCode0
Style-Aware Contrastive Learning for Multi-Style Image Captioning—0
CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization—0
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
Few-shot Font Generation by Learning Style Difference and Similarity—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning—0
Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identificationCode0
Causality-based Dual-Contrastive Learning Framework for Domain Generalization—0
ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition—0
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its ApplicationsCode0
Semantic-aware Contrastive Learning for More Accurate Semantic Parsing—0
Joint Representation Learning for Text and 3D Point CloudCode0
Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-trainingCode0
Temporal Perceiving Video-Language Pre-training—0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data—0
USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text RetrievalCode0
Linguistic Query-Guided Mask Generation for Referring Image Segmentation—0
MN-Pair Contrastive Damage Representation and Clustering for Prognostic Explanation—0
Exploiting Auxiliary Caption for Video Grounding—0
FedSSC: Shared Supervised-Contrastive Federated Learning—0
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation—0
Learning to Summarize Videos by Contrasting Clips—0
Unsupervised Driving Event Discovery Based on Vehicle CAN-data—0
Signed Directed Graph Contrastive Learning with Laplacian Augmentation—0
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss—0
Pylon: Semantic Table Union Search in Data LakesCode0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input—0
Anomalies, Representations, and Self-Supervision—0
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration—0
Heterogeneous Tri-stream Clustering NetworkCode0
Metric Compatible Training for Online Backfilling in Large-Scale Retrieval—0
From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore—0
CDA: Contrastive-adversarial Domain Adaptation—0
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG—0
Simplifying Open-Set Video Domain Adaptation with Contrastive LearningCode0
Mitigating Human and Computer Opinion Fraud via Contrastive Learning—0
Multi-scale multi-modal micro-expression recognition algorithm based on transformer—0
Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
Event Camera Data Pre-training—0
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST—0
MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small DatasetsCode0
Episodes Discovery Recommendation with Multi-Source Augmentations—0
Surveillance Face Anti-spoofing—0
Multi-View Action Recognition Using Contrastive LearningCode0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified