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 3201–3225 of 6661 papers

TitleStatusHype
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis—0
Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function Contrastive Learning of Transferable Representations—0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
Indoor Smartphone SLAM with Learned Echoic Location Features—0
Contrastive Learning for Time Series on Dynamic Graphs—0
INDUS: Effective and Efficient Language Models for Scientific Applications—0
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection—0
Learning Differentiable Surrogate Losses for Structured Prediction—0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Learning Discriminative Features for Crowd Counting—0
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification—0
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach—0
Information-Aware Time Series Meta-Contrastive Learning—0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—0
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
Information Maximization for Extreme Pose Face Recognition—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
3D-Aware Encoding for Style-based Neural Radiance Fields—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—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