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 26–50 of 6661 papers

TitleStatusHype
DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation—0
Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive LearningCode0
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud RecognitionCode0
Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous GraphsCode0
PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching—0
V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling—0
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer FeaturesCode0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical VisionCode1
scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection—0
ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations—0
Directed Link Prediction using GNN with Local and Global Feature Fusion—0
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning—0
Iterative Quantum Feature Maps—0
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECGCode0
WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT—0
VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT RegistrationCode0
SSAVSV: Towards Unified Model for Self-Supervised Audio-Visual Speaker Verification—0
Metapath-based Hyperbolic Contrastive Learning for Heterogeneous Graph Embedding—0
A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation—0
Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities—0
Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images—0
Heterogeneous Temporal Hypergraph Neural Network—0
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited PlacesCode0
I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs—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