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 5626–5650 of 6661 papers

TitleStatusHype
Explicit View-labels Matter: A Multifacet Complementarity Study of Multi-view Clustering—0
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training—0
Lexical Knowledge Internalization for Neural Dialog GenerationCode0
Analysing the Robustness of Dual Encoders for Dense Retrieval Against MisspellingsCode0
Do More Negative Samples Necessarily Hurt in Contrastive Learning?—0
i-Code: An Integrative and Composable Multimodal Learning Framework—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification—0
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource LanguagesCode0
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language UnderstandingCode0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
When does CLIP generalize better than unimodal models? When judging human-centric concepts—0
Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training—0
Mitigating Contradictions in Dialogue Based on Contrastive Learning—0
Syntax-guided Contrastive Learning for Pre-trained Language Model—0
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining—0
NYCU_TWD@LT-EDI-ACL2022: Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media—0
UTC: A Unified Transformer with Inter-Task Contrastive Learning for Visual Dialog—0
Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model—0
Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders—0
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning—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