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 3126–3150 of 6661 papers

TitleStatusHype
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery—0
Game State Learning via Game Scene Augmentation—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Improving Generalizability of Protein Sequence Models via Data Augmentations—0
Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control—0
Contrastive Learning from Demonstrations—0
GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations—0
Improving Graph Contrastive Learning via Adaptive Positive Sampling—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Contrastive Learning for View Classification of Echocardiograms—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness—0
Contrastive Learning for Unsupervised Video Highlight Detection—0
A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
Improving Micro-video Recommendation by Controlling Position Bias—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Improving Neural Topic Models by Contrastive Learning with BERT—0
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition—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