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 2651–2675 of 6661 papers

TitleStatusHype
The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation—0
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT—0
Isolating authorship from content with semantic embeddings and contrastive learning—0
From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps—0
Multi-Label Contrastive Learning : A Comprehensive StudyCode0
Manual-PA: Learning 3D Part Assembly from Instruction Diagrams—0
Novel Class Discovery for Open Set Raga Classification—0
Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and GroundingCode0
MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting—0
Structure-Guided MR-to-CT Synthesis with Spatial and Semantic Alignments for Attenuation Correction of Whole-Body PET/MR Imaging—0
Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time AdaptationCode0
MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic Segmentation Network For Relic Landslide Detection—0
DWCL: Dual-Weighted Contrastive Learning for Multi-View ClusteringCode0
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning—0
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders—0
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification—0
Abnormality-Driven Representation Learning for Radiology Imaging—0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation—0
Boosting Semi-Supervised Scene Text Recognition via Viewing and SummarizingCode0
Point Cloud Understanding via Attention-Driven Contrastive Learning—0
An Attention-based Framework for Fair Contrastive Learning—0
Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization—0
Context-Aware Multimodal Pretraining—0
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems—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