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 4701–4725 of 6661 papers

TitleStatusHype
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive LearningCode0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Is Cross-modal Information Retrieval Possible without Training?—0
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis—0
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision BoundaryCode0
ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
Shuffle & Divide: Contrastive Learning for Long Text—0
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation—0
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training—0
EC^2: Emergent Communication for Embodied Control—0
Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite ImageryCode0
Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata—0
D2CSE: Difference-aware Deep continuous prompts for Contrastive Sentence Embeddings—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning—0
Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference—0
Multimodal Short Video Rumor Detection System Based on Contrastive Learning—0
VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning—0
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation—0
MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation—0
Medical Question Summarization with Entity-driven Contrastive LearningCode0
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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