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 5726–5750 of 6661 papers

TitleStatusHype
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
MOSMOS: Multi-organ segmentation facilitated by medical report supervision—0
Motif-Driven Contrastive Learning of Graph Representations—0
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation—0
MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation—0
Motion Sensitive Contrastive Learning for Self-supervised Video Representation—0
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation—0
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning—0
Movies2Scenes: Using Movie Metadata to Learn Scene Representation—0
WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal—0
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence—0
MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation—0
MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic Segmentation Network For Relic Landslide Detection—0
MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation—0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment—0
MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
Multi-behavior Recommendation with SVD Graph Neural Networks—0
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID—0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation—0
Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection—0
Multi-Domain Learning From Insufficient Annotations—0
Multi-Domain Self-Supervised Learning—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