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 5601–5625 of 6661 papers

TitleStatusHype
MarginNCE: Robust Sound Localization with a Negative Margin—0
MaRI: Material Retrieval Integration across Domains—0
MarkMatch: Same-Hand Stuffing Detection—0
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog—0
Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation—0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning—0
Masked Audio Text Encoders are Effective Multi-Modal Rescorers—0
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation—0
Masked Contrastive Reconstruction for Cross-modal Medical Image-Report Retrieval—0
Masked Contrastive Representation Learning—0
Masked Image Contrastive Learning for Efficient Visual Conceptual Pre-training—0
Masked Image Modeling Advances 3D Medical Image Analysis—0
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain—0
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning—0
Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound—0
Mask-guided BERT for Few Shot Text Classification—0
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring—0
Massively Scaling Heteroscedastic Classifiers—0
MatExpert: Decomposing Materials Discovery by Mimicking Human Experts—0
X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs—0
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem—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