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 2451–2500 of 6661 papers

TitleStatusHype
Encoding Binary Events from Continuous Time Series in Rooted Trees using Contrastive Learning—0
Revisiting Counterfactual Problems in Referring Expression ComprehensionCode0
PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-wise HardnessCode1
PeVL: Pose-Enhanced Vision-Language Model for Fine-Grained Human Action Recognition—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
Shallow-Deep Collaborative Learning for Unsupervised Visible-Infrared Person Re-IdentificationCode1
MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning—0
Self-Supervised Representation Learning from Arbitrary Scenarios—0
Building Vision-Language Models on Solid Foundations with Masked Distillation—0
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing—0
Weakly-Supervised Audio-Visual Video Parsing with Prototype-based Pseudo-Labeling—0
Fair-VPT: Fair Visual Prompt Tuning for Image Classification—0
Positive-Unlabeled Learning by Latent Group-Aware Meta DisambiguationCode1
Prompt-Driven Referring Image Segmentation with Instance Contrasting—0
Instance-aware Contrastive Learning for Occluded Human Mesh ReconstructionCode1
Improving Graph Contrastive Learning via Adaptive Positive Sampling—0
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning—0
Density-Guided Semi-Supervised 3D Semantic Segmentation with Dual-Space Hardness Sampling—0
Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
Weakly Supervised Video Individual CountingCode1
Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning—0
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking—0
Enhancing Post-training Quantization Calibration through Contrastive Learning—0
Contextual Augmented Global Contrast for Multimodal Intent Recognition—0
Embracing Unimodal Aleatoric Uncertainty for Robust Multimodal Fusion—0
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIPCode0
Analyzing Local Representations of Self-supervised Vision Transformers—0
Contrastive learning-based agent modeling for deep reinforcement learning—0
Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence RegularizationCode1
QGFace: Quality-Guided Joint Training For Mixed-Quality Face Recognition—0
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering—0
Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion—0
Learning Vision from Models Rivals Learning Vision from DataCode2
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs—0
A Contrastive Variational Graph Auto-Encoder for Node ClusteringCode0
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text PromptsCode2
Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics—0
Learning to Embed Time Series Patches IndependentlyCode1
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting—0
Soft Contrastive Learning for Time SeriesCode1
scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive LearningCode0
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks—0
MIM4DD: Mutual Information Maximization for Dataset Distillation—0
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation—0
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data LimitationsCode0
Medical Report Generation based on Segment-Enhanced Contrastive Representation Learning—0
Masked Contrastive Reconstruction for Cross-modal Medical Image-Report Retrieval—0
Federated Hyperdimensional Computing—0
TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation LearningCode1
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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