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 3276–3300 of 6661 papers

TitleStatusHype
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser—0
Diffusion Models as Masked Audio-Video Learners—0
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation—0
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter—0
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity—0
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation—0
DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts—0
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction—0
Difficulty-Based Sampling for Debiased Contrastive Representation Learning—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—0
Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation—0
DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
Dictionary Learning by Dynamical Neural Networks—0
Dictionary-based Framework for Interpretable and Consistent Object Parsing—0
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
CLOUD: Contrastive Learning of Unsupervised Dynamics—0
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition—0
DICE: Data-Efficient Clinical Event Extraction with Generative Models—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