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 4951–5000 of 6661 papers

TitleStatusHype
Towards a Visual-Language Foundation Model for Computational Pathology—0
Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective—0
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
Towards Channel-Resilient CSI-Based RF Fingerprinting using Deep Learning—0
Towards Contrastive Learning in Music Video Domain—0
Towards Cross-domain Few-shot Graph Anomaly Detection—0
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning—0
Towards Discriminative Representation Learning for Unsupervised Person Re-identification—0
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking—0
Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking—0
Towards Domain-Agnostic Contrastive Learning—0
Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation—0
Towards EEG signals codification using contrastiveloss—0
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing—0
Towards Efficient Contrastive PAC Learning—0
Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context—0
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
Towards Generalisable Audio Representations for Audio-Visual Navigation—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Towards Generalized Multi-stage Clustering: Multi-view Self-distillation—0
Towards General Text Embeddings with Multi-stage Contrastive Learning—0
Towards Generating Realistic Underwater Images—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images—0
Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning—0
Towards Less Biased Data-driven Scoring with Deep Learning-Based End-to-end Database Search in Tandem Mass Spectrometry—0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts—0
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering—0
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data—0
Towards More Generalizable One-shot Visual Imitation Learning—0
Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning—0
Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering—0
Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion—0
Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words—0
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning—0
Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering—0
Towards NSFW-Free Text-to-Image Generation via Safety-Constraint Direct Preference Optimization—0
Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction—0
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation—0
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information—0
Towards Robust and Efficient Contrastive Textual Representation Learning—0
Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation—0
Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies—0
Towards Robust Graph Contrastive Learning—0
Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning—0
Towards Robust Textual Representations with Disentangled Contrastive Learning—0
Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning—0
Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods—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