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 1926–1950 of 6661 papers

TitleStatusHype
Factual Dialogue Summarization via Learning from Large Language Models—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures—0
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training—0
CodeFort: Robust Training for Code Generation Models—0
Augmentation-Free Graph Contrastive Learning with Performance Guarantee—0
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease—0
Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis—0
Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision—0
Augmentation adversarial training for self-supervised speaker recognition—0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning—0
Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations—0
Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
Audio-Visual Contrastive Learning with Temporal Self-Supervision—0
Self-supervised Contrastive Learning for Audio-Visual Action Recognition—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering—0
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
CoCGAN: Contrastive Learning for Adversarial Category Text Generation—0
Feedback Reciprocal Graph Collaborative Filtering—0
Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—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