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 4776–4800 of 6661 papers

TitleStatusHype
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training—0
ESCL: Equivariant Self-Contrastive Learning for Sentence Representations—0
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning—0
ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes—0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—0
Evaluating unsupervised contrastive learning framework for MRI sequences classification—0
Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels—0
Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection—0
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models—0
Event-aware Video Corpus Moment Retrieval—0
Event Camera Data Pre-training—0
Event-Centric Query Expansion in Web Search—0
Evidential Graph Contrastive Alignment for Source-Free Blending-Target Domain Adaptation—0
Evolutionary Contrastive Distillation for Language Model Alignment—0
Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning—0
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media—0
Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning—0
Explicit homography estimation improves contrastive self-supervised learning—0
Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking—0
Exploiting Data Hierarchy as a New Modality for Contrastive Learning—0
Exploiting Low-confidence Pseudo-labels for Source-free Object Detection—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