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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 6601–6650 of 6661 papers

TitleStatusHype
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
Audio-Visual Instance Discrimination with Cross-Modal AgreementCode1
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive LearningCode1
Supervised Contrastive LearningCode2
Distilling Localization for Self-Supervised Representation Learning—0
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
Clustering based Contrastive Learning for Improving Face Representations—0
Edge Guided GANs with Contrastive Learning for Semantic Image SynthesisCode1
Semi-supervised Contrastive Learning Using Partial Label Information—0
On Compositions of Transformations in Contrastive Self-Supervised LearningCode1
Improved Baselines with Momentum Contrastive LearningCode1
Contrastive estimation reveals topic posterior information to linear models—0
CoLES: Contrastive Learning for Event Sequences with Self-SupervisionCode1
Convergence of End-to-End Training in Deep Unsupervised Contrastive Learning—0
A Simple Framework for Contrastive Learning of Visual RepresentationsCode2
On Contrastive Learning for Likelihood-free InferenceCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
Understanding Contrastive Representation Learning through Geometry on the HypersphereCode1
Self-Supervised Learning of Pretext-Invariant RepresentationsCode1
Contrastive Learning of Structured World ModelsCode0
Self-labelling via simultaneous clustering and representation learningCode1
Momentum Contrast for Unsupervised Visual Representation LearningCode3
Contrastive Multi-document Question GenerationCode0
Robust contrastive learning and nonlinear ICA in the presence of outliers—0
Contrastive Representation DistillationCode1
Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision—0
Annotated Guidelines and Building Reference Corpus for Myanmar-English Word Alignment—0
Large Scale Adversarial Representation LearningCode1
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach—0
Contrastive Multiview CodingCode1
Online Object Representations with Contrastive Learning—0
Unified Visual-Semantic Embeddings: Bridging Vision and Language With Structured Meaning RepresentationsCode0
Hebbian-Descent—0
Data-Efficient Image Recognition with Contrastive Predictive CodingCode0
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification—0
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning—0
Adversarial Defense Framework for Graph Neural Network—0
Contrastive Learning for Lifted Networks—0
Sparse Dictionary Learning by Dynamical Neural Networks—0
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic RepresentationsCode1
Local Aggregation for Unsupervised Learning of Visual EmbeddingsCode0
Statistically-informed deep learning for gravitational wave parameter estimation—0
A Theoretical Analysis of Contrastive Unsupervised Representation Learning—0
Contrastive Variational Autoencoder Enhances Salient FeaturesCode0
Contrastive Learning from Pairwise Measurements—0
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document SummarizationCode0
Contrastive Video Representation Learning via Adversarial Perturbations—0
Mask-Guided Contrastive Attention Model for Person Re-IdentificationCode0
Dictionary Learning by Dynamical Neural Networks—0
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive LearningCode0
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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