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 2451–2475 of 6661 papers

TitleStatusHype
TCMM: Token Constraint and Multi-Scale Memory Bank of Contrastive Learning for Unsupervised Person Re-identificationCode0
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing—0
Advancing Brainwave-Based Biometrics: A Large-Scale, Multi-Session EvaluationCode0
Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions—0
Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders—0
ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression—0
Graph Contrastive Learning on Multi-label Classification for Recommendations—0
Evaluating unsupervised contrastive learning framework for MRI sequences classification—0
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning—0
Learning Compact and Robust Representations for Anomaly Detection—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
Quantum-inspired Embeddings Projection and Similarity Metrics for Representation LearningCode0
Multimodal Graph Constrastive Learning and Prompt for ChartQA—0
MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation—0
VOILA: Complexity-Aware Universal Segmentation of CT images by Voxel Interacting with LanguageCode0
Action Quality Assessment via Hierarchical Pose-guided Multi-stage Contrastive RegressionCode0
Discriminative Representation learning via Attention-Enhanced Contrastive Learning for Short Text ClusteringCode0
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving—0
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value IdentificationCode0
CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds—0
An Empirical Study of Accuracy-Robustness Tradeoff and Training Efficiency in Self-Supervised LearningCode0
Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation LearningCode0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—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