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 4601–4625 of 6661 papers

TitleStatusHype
Domain Adaptive Lung Nodule Detection in X-ray Image—0
Domain-Aware Augmentations for Unsupervised Online General Continual Learning—0
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation—0
Domain Contrast for Domain Adaptive Object Detection—0
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)—0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data—0
Domain Prompt Learning with Quaternion Networks—0
Do More Negative Samples Necessarily Hurt in Contrastive Learning?—0
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning—0
Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation—0
Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis—0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning—0
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation—0
DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm—0
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers—0
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images—0
DR-BFR: Degradation Representation with Diffusion Models for Blind Face Restoration—0
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes—0
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age—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