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 1951–1975 of 6661 papers

TitleStatusHype
Self-supervised Contrastive Learning for Audio-Visual Action Recognition—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Fair Node Representation Learning via Adaptive Data Augmentation—0
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
Do Audio-Language Models Understand Linguistic Variations?—0
FAMSeC: A Few-shot-sample-based General AI-generated Image Detection Method—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
CoCGAN: Contrastive Learning for Adversarial Category Text Generation—0
Feedback Reciprocal Graph Collaborative Filtering—0
Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality—0
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering—0
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis—0
Decentralized Unsupervised Learning of Visual Representations—0
Audio Contrastive based Fine-tuning—0
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization—0
Distributed Contrastive Learning for Medical Image Segmentation—0
Domain Adaptive Lung Nodule Detection in X-ray Image—0
Distortion-Disentangled Contrastive Learning—0
Domain-Aware Augmentations for Unsupervised Online General Continual Learning—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—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