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 3051–3100 of 6661 papers

TitleStatusHype
Decoupled Contrastive Multi-View Clustering with High-Order Random WalksCode1
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for RecommendationCode1
Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking—0
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based FeaturesCode1
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
Contrastive Graph Prompt-tuning for Cross-domain Recommendation—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Contrastive Learning based Deep Latent Masking for Music Source Separation—0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language UnderstandingCode0
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRsCode0
Robust Fraud Detection via Supervised Contrastive Learning—0
3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose EstimationCode1
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views—0
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised ClusteringCode1
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
Decoupled conditional contrastive learning with variable metadata for prostate lesion detectionCode0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation LearningCode1
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
Investigation of Architectures and Receptive Fields for Appearance-based Gaze EstimationCode1
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning—0
URL: Combating Label Noise for Lung Nodule Malignancy GradingCode0
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
Hyperbolic Face Anti-Spoofing—0
MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling—0
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries—0
Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification—0
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval—0
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment—0
Contrastive Learning for Lane Detection via cross-similarityCode0
MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple DistractorsCode0
AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing—0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation—0
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems—0
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition—0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision—0
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks—0
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label NoiseCode1
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingCode1
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach—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