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 5801–5825 of 6661 papers

TitleStatusHype
Generating Compositional Color Representations from Text—0
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style EditingCode1
VPN: Video Provenance Network for Robust Content Attribution—0
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Homography augumented momentum constrastive learning for SAR image retrieval—0
On the Importance of Distractors for Few-Shot ClassificationCode1
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language ModelsCode1
Adversarial Training with Contrastive Learning in NLP—0
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive SummarizationCode0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
Interest-oriented Universal User Representation via Contrastive Learning—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Semi-Supervised Few-Shot Intent Classification and Slot Filling—0
Pointly-supervised 3D Scene Parsing with Viewpoint BottleneckCode1
Dense Semantic Contrast for Self-Supervised Visual Representation Learning—0
Self-supervised Contrastive Learning for EEG-based Sleep StagingCode1
Federated Contrastive Learning for Decentralized Unlabeled Medical Images—0
Semi-supervised Contrastive Learning for Label-efficient Medical Image SegmentationCode1
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence RepresentationsCode1
Deep Bregman Divergence for Contrastive Learning of Visual Representations—0
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive LearningCode1
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-IdentificationCode0
KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning—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