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 2476–2500 of 6661 papers

TitleStatusHype
A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing—0
Heterogeneous bimodal attention fusion for speech emotion recognition—0
Heterogeneous Temporal Hypergraph Neural Network—0
Cross-domain Transfer of defect features in technical domains based on partial target data—0
Challenging Low Homophily in Social Recommendation—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Adversarial Training with Contrastive Learning in NLP—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Cross-Domain Document Layout Analysis Using Document Style Guide—0
CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing—0
Cross-Domain 3D Hand Pose Estimation With Dual Modalities—0
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play—0
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data—0
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations—0
CFTrack: Enhancing Lightweight Visual Tracking through Contrastive Learning and Feature Matching—0
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning—0
A Scalable Holistic approach for Age and Gender inference of Twitter Users—0
Cross-Age Contrastive Learning for Age-Invariant Face Recognition—0
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
Hebbian-Descent—0
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach—0
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies—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