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 1526–1550 of 6661 papers

TitleStatusHype
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis—0
Discriminative and Consistent Representation DistillationCode1
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks—0
Finding Meaning in Points: Weakly Supervised Semantic Segmentation for Event CamerasCode1
Towards Robust Event-based Networks for Nighttime via Unpaired Day-to-Night Event TranslationCode0
Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation—0
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton SequencesCode1
Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
Cross-Lingual Multi-Hop Knowledge Editing -- Benchmarks, Analysis and a Simple Contrastive Learning based Approach—0
Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework—0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—0
URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering—0
On the Role of Discrete Tokenization in Visual Representation LearningCode0
Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action LocalizationCode0
One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive LearningCode0
Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media—0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text—0
Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification—0
MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data EngineCode4
From Real to Cloned Singer IdentificationCode1
Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement—0
Multimodal contrastive learning for spatial gene expression prediction using histology imagesCode1
AddressCLIP: Empowering Vision-Language Models for City-wide Image Address LocalizationCode2
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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