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 4901–4925 of 6661 papers

TitleStatusHype
Few-shot Font Generation by Learning Style Difference and Similarity—0
Few-shot Implicit Function Generation via Equivariance—0
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection—0
Few-Shot Nested Named Entity Recognition—0
Few-shot Object Detection with Refined Contrastive Learning—0
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin—0
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images—0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention—0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
Few-shot Text Classification with Dual Contrastive Consistency—0
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning—0
FewUser: Few-Shot Social User Geolocation via Contrastive Learning—0
FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics—0
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge—0
FIESTA: Autoencoders for accurate fiber segmentation in tractography—0
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization—0
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning—0
Fine-Grained Classification with Noisy Labels—0
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Fine Grained Insider Risk Detection—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
Fine-grained Software Vulnerability Detection via Information Theory and 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