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 3976–4000 of 6661 papers

TitleStatusHype
NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide ImagesCode0
Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment RetrievalCode0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
Supervised Contrastive Learning for Fine-grained Chromosome Recognition—0
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationCode0
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms—0
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks—0
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity—0
RCA-NOC: Relative Contrastive Alignment for Novel Object Captioning—0
Temporal Supervised Contrastive Learning for Modeling Patient Risk ProgressionCode0
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting—0
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction—0
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models—0
Understanding Community Bias Amplification in Graph Representation Learning—0
Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental ClusteringCode0
Damage GAN: A Generative Model for Imbalanced Data—0
StructComp: Substituting Propagation with Structural Compression in Training Graph Contrastive LearningCode0
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations—0
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift—0
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn GraphsCode0
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series—0
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds—0
Graph Information Bottleneck for Remote Sensing Segmentation—0
Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry—0
Rethinking and Simplifying Bootstrapped Graph LatentsCode0
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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