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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 6376–6400 of 6661 papers

TitleStatusHype
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations—0
IB-DRR: Incremental Learning with Information-Back Discrete Representation Replay—0
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training—0
A Framework using Contrastive Learning for Classification with Noisy Labels—0
Self-Supervised WiFi-Based Activity Recognition—0
A Semi-Supervised Classification Method of Apicomplexan Parasites and Host Cell Using Contrastive Learning Strategy—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop—0
Constructing Contrastive samples via Summarization for Text Classification with limited annotationsCode0
Disentangled Contrastive Learning for Learning Robust Textual RepresentationsCode0
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—0
Scene Graph Embeddings Using Relative Similarity Supervision—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Strumming to the Beat: Audio-Conditioned Contrastive Video Textures—0
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning—0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets—0
Unsupervised Sound Localization via Iterative Contrastive Learning—0
Composable Augmentation Encoding for Video Representation Learning—0
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information—0
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification—0
Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks—0
Robust Audio-Visual Instance Discrimination—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