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 4451–4475 of 6661 papers

TitleStatusHype
Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive LearningCode0
QontSum: On Contrasting Salient Content for Query-focused Summarization—0
Composition-contrastive Learning for Sentence EmbeddingsCode0
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation—0
Multi-view self-supervised learning for multivariate variable-channel time seriesCode0
Unified Medical Image-Text-Label Contrastive Learning With Continuous Prompt—0
Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning—0
The Whole Pathological Slide Classification via Weakly Supervised Learning—0
CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction—0
Class Instance Balanced Learning for Long-Tailed Classification—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text ClassificationCode0
Disentangled Contrastive Image Translation for Nighttime Surveillance—0
Generalizing Graph ODE for Learning Complex System Dynamics across Environments—0
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—0
Hate Speech Detection via Dual Contrastive Learning—0
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning—0
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics dataCode0
End-to-End Supervised Multilabel Contrastive LearningCode0
Weakly-supervised Contrastive Learning for Unsupervised Object DiscoveryCode0
AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation—0
Polybot: Training One Policy Across Robots While Embracing Variability—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