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 6326–6350 of 6661 papers

TitleStatusHype
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation—0
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning—0
Self-Supervised Alignment Learning for Medical Image Segmentation—0
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations—0
Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks—0
Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning—0
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval—0
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis—0
Self-supervised Contrastive Attributed Graph Clustering—0
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents—0
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation—0
Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI—0
Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems—0
Self-supervised Contrastive Learning for Implicit Collaborative Filtering—0
Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning—0
Self-supervised Contrastive Learning of Multi-view Facial Expressions—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation—0
Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
Self-supervised Document Clustering Based on BERT with Data Augment—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG—0
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—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