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 4351–4375 of 6661 papers

TitleStatusHype
Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRsCode0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
Decoupled conditional contrastive learning with variable metadata for prostate lesion detectionCode0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification—0
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries—0
Hyperbolic Face Anti-Spoofing—0
URL: Combating Label Noise for Lung Nodule Malignancy GradingCode0
MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling—0
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning—0
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval—0
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment—0
Contrastive Learning for Lane Detection via cross-similarityCode0
MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple DistractorsCode0
AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing—0
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation—0
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems—0
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision—0
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks—0
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition—0
Contrastive Learning for Cross-modal Artist Retrieval—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