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 3076–3100 of 6661 papers

TitleStatusHype
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
Investigation of Architectures and Receptive Fields for Appearance-based Gaze EstimationCode1
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning—0
URL: Combating Label Noise for Lung Nodule Malignancy GradingCode0
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
Hyperbolic Face Anti-Spoofing—0
MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling—0
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries—0
Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification—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
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation—0
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems—0
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition—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
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label NoiseCode1
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingCode1
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach—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