SOTAVerified

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 5051–5100 of 6661 papers

TitleStatusHype
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images—0
Two-stream joint matching method based on contrastive learning for few-shot action recognition—0
U2-Former: A Nested U-shaped Transformer for Image Restoration—0
UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection—0
UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection—0
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining—0
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control—0
UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition—0
UMGAD: Unsupervised Multiplex Graph Anomaly Detection—0
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning—0
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation—0
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation—0
Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance—0
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks—0
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning—0
Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting—0
Understand and Improve Contrastive Learning Methods for Visual Representation: A Review—0
Understanding and Improving the Role of Projection Head in Self-Supervised Learning—0
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training—0
Understanding Community Bias Amplification in Graph Representation Learning—0
Understanding Contrastive Learning Requires Incorporating Inductive Biases—0
Understanding Contrastive Learning Through the Lens of Margins—0
Understanding Contrastive Learning via Gaussian Mixture Models—0
Understanding Difficult-to-learn Examples in Contrastive Learning: A Theoretical Framework for Spectral Contrastive Learning—0
Understanding Masked Autoencoders From a Local Contrastive Perspective—0
Understanding Masked Image Modeling via Learning Occlusion Invariant Feature—0
Weighted Point Cloud Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric—0
Understanding Self-supervised Learning via Information Bottleneck Principle—0
Understanding the Behaviour of Contrastive Loss—0
Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift—0
Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning—0
Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition—0
Unearthing Common Inconsistency for Generalisable Deepfake Detection—0
UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All—0
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning—0
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning—0
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning—0
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition—0
Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval—0
Unified Medical Image-Text-Label Contrastive Learning With Continuous Prompt—0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
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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