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 2801–2825 of 6661 papers

TitleStatusHype
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Graph Contrastive Pre-training for Effective Theorem Reasoning—0
GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation—0
Bridging the Emotional Semantic Gap via Multimodal Relevance Estimation—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification—0
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience—0
Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Contrastive Learning of Sentence Representations—0
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
Contrastive Learning with Positive-Negative Frame Mask for Music Representation—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Graph Representation Learning via Contrasting Cluster Assignments—0
Graph Self-Contrast Representation Learning—0
A Chinese Spelling Check Framework Based on Reverse Contrastive Learning—0
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
GraphTTA: Test Time Adaptation on Graph Neural Networks—0
Contrastive Learning of Preferences with a Contextual InfoNCE Loss—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