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 4776–4800 of 6661 papers

TitleStatusHype
Model and Evaluation: Towards Fairness in Multilingual Text Classification—0
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding—0
Text-to-Image Diffusion Models are Zero-Shot ClassifiersCode0
Meeting Action Item Detection with Regularized Context Modeling—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
A Contrastive Learning Scheme with Transformer Innate PatchesCode0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions—0
Selective Structured State-Spaces for Long-Form Video Understanding—0
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic SegmentationCode0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
Local Contrastive Learning for Medical Image Recognition—0
Hybrid Augmented Automated Graph Contrastive Learning—0
WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal—0
Aligning Step-by-Step Instructional Diagrams to Video DemonstrationsCode0
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning—0
Adaptive Similarity Bootstrapping for Self-Distillation based Representation LearningCode0
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology—0
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains—0
Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning—0
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning—0
Multi-view Feature Extraction based on Triple Contrastive Heads—0
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder—0
Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—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