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 3401–3425 of 6661 papers

TitleStatusHype
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning—0
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest NeighborsCode0
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization—0
UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based RecommendationCode1
TabGSL: Graph Structure Learning for Tabular Data Prediction—0
Contrastive Training of Complex-Valued Autoencoders for Object DiscoveryCode0
Clinically Labeled Contrastive Learning for OCT Biomarker Classification—0
Contrastive Learning of Sentence Embeddings from ScratchCode1
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification—0
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent ClassificationCode1
Improving Factuality of Abstractive Summarization without Sacrificing Summary QualityCode0
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist CaptionsCode1
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine TranslationCode1
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text ClusteringCode1
Real-Time Idling Vehicles Detection using Combined Audio-Visual Deep Learning—0
On Learning to Summarize with Large Language Models as ReferencesCode1
Optimizing Non-Autoregressive Transformers with Contrastive Learning—0
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text EmbeddingsCode1
Temporal Contrastive Learning for Spiking Neural Networks—0
SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes ChangeCode1
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative TrainingCode5
SAD: Semi-Supervised Anomaly Detection on Dynamic GraphsCode1
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound ClassificationCode1
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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