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 2776–2800 of 6661 papers

TitleStatusHype
Contrastive Learning of Visual-Semantic Embeddings—0
Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning—0
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Contrastive Learning Via Equivariant Representation—0
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition—0
Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems—0
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution—0
Improving Neural Topic Models by Contrastive Learning with BERT—0
Improving Radiology Summarization with Radiograph and Anatomy Prompts—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniques—0
Graph Contrastive Learning for Multi-omics Data—0
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
Graph Contrastive Learning on Multi-label Classification for Recommendations—0
Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification—0
Improving Graph Contrastive Learning via Adaptive Positive Sampling—0
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
GLCC: A General Framework for Graph-Level Clustering—0
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization—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