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 51–75 of 6661 papers

TitleStatusHype
I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs—0
Unified Representation Space for 3D Visual Grounding—0
Refining music sample identification with a self-supervised graph neural networkCode1
SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation—0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
TR2M: Transferring Monocular Relative Depth to Metric Depth with Language Descriptions and Scale-Oriented ContrastCode1
Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval—0
RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning—0
Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems—0
Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and ExplainabilityCode0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning—0
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics—0
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition—0
PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image AnalysisCode0
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning—0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data—0
ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory ForecastingCode0
Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image ClusteringCode0
On the Similarities of Embeddings in Contrastive LearningCode1
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
Generalizing Supervised Contrastive learning: A Projection Perspective—0
Probabilistic Variational Contrastive Learning—0
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision ModelsCode1
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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