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 2101–2125 of 6661 papers

TitleStatusHype
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model—0
MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation—0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—0
DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—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
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—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
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
Fine-grained Text to Image Synthesis—0
Dictionary Learning by Dynamical Neural Networks—0
Dictionary-based Framework for Interpretable and Consistent Object Parsing—0
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition—0
CLOUD: Contrastive Learning of Unsupervised Dynamics—0
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
Fine Grained Insider Risk Detection—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
DICE: Data-Efficient Clinical Event Extraction with Generative Models—0
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification—0
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking—0
Dialogue Response Generation via Contrastive Latent Representation Learning—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