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 3576–3600 of 6661 papers

TitleStatusHype
Learn What NOT to Learn: Towards Generative Safety in Chatbots—0
Deep Multiview Clustering by Contrasting Cluster AssignmentsCode1
MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive LearningCode0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis—0
Is Cross-modal Information Retrieval Possible without Training?—0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision BoundaryCode0
Contrastive Tuning: A Little Help to Make Masked Autoencoders ForgetCode1
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
ID-MixGCL: Identity Mixup for Graph Contrastive Learning—0
ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode—0
Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite ImageryCode0
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation—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
EC^2: Emergent Communication for Embodied Control—0
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image UnderstandingCode1
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training—0
Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata—0
Frequency Enhanced Hybrid Attention Network for Sequential RecommendationCode1
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
D2CSE: Difference-aware Deep continuous prompts for Contrastive Sentence Embeddings—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
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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