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 2401–2425 of 6661 papers

TitleStatusHype
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
CktGen: Specification-Conditioned Analog Circuit Generation—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios—0
A Siamese Network to Detect If Two Iris Images Are Monozygotic—0
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning—0
CI w/o TN: Context Injection without Task Name for Procedure Planning—0
A contrastive learning approach for individual re-identification in a wild fish population—0
CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services—0
CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors—0
CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization—0
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking—0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
A Framework For Image Synthesis Using Supervised Contrastive Learning—0
3D Neural Scene Representations for Visuomotor Control—0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization—0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity—0
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass—0
Graph Soft-Contrastive Learning via Neighborhood Ranking—0
C-SENN: Contrastive Self-Explaining Neural Network—0
CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis—0
CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction—0
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings—0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—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