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
Audio-Visual Contrastive Learning with Temporal Self-Supervision—0
DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
Self-supervised Contrastive Learning for Audio-Visual Action Recognition—0
Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
CoCGAN: Contrastive Learning for Adversarial Category Text Generation—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality—0
Decentralized Unsupervised Learning of Visual Representations—0
Distributed Contrastive Learning for Medical Image Segmentation—0
Distortion-Disentangled Contrastive Learning—0
Audio Contrastive based Fine-tuning—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
A two-steps approach to improve the performance of Android malware detectors—0
AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify—0
Distilling Localization for Self-Supervised Representation Learning—0
CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization—0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—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