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 1926–1950 of 6661 papers

TitleStatusHype
A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation—0
Metapath-based Hyperbolic Contrastive Learning for Heterogeneous Graph Embedding—0
Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities—0
Heterogeneous Temporal Hypergraph Neural Network—0
Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images—0
CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs—0
Unified Representation Space for 3D Visual Grounding—0
I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs—0
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited PlacesCode0
Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems—0
Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval—0
RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning—0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation—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
PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image AnalysisCode0
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition—0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning—0
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data—0
ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory ForecastingCode0
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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