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 3176–3200 of 6661 papers

TitleStatusHype
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection—0
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation—0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Domain Contrast for Domain Adaptive Object Detection—0
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training—0
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation—0
CodeFort: Robust Training for Code Generation Models—0
Augmentation-Free Graph Contrastive Learning with Performance Guarantee—0
Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures—0
Domain-Aware Augmentations for Unsupervised Online General Continual Learning—0
Domain Adaptive Lung Nodule Detection in X-ray Image—0
Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis—0
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization—0
Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning—0
Augmentation adversarial training for self-supervised speaker recognition—0
Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision—0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning—0
DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval—0
DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering—0
Do Audio-Language Models Understand Linguistic Variations?—0
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—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