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 5526–5550 of 6661 papers

TitleStatusHype
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning—0
Rethinking Audio-visual Synchronization for Active Speaker Detection—0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Visualizing and Understanding Contrastive LearningCode0
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval—0
C-SENN: Contrastive Self-Explaining Neural Network—0
Self-Supervised Learning for Videos: A SurveyCode0
Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection—0
DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images—0
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix—0
Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation—0
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation LearningCode0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
Towards a Solution to Bongard Problems: A Causal Approach—0
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis—0
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection—0
Self-Supervised Representation Learning With MUlti-Segmental Informational Coding (MUSIC)—0
Transductive CLIP with Class-Conditional Contrastive Learning—0
Is Self-Supervised Learning More Robust Than Supervised Learning?—0
ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation—0
STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer—0
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation—0
Words are all you need? Language as an approximation for human similarity judgments—0
Contrastive Graph Multimodal Model for Text Classification in Videos—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—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