Papers Unsupervised MNIST
“Unsupervised MNIST” 태그가 달린 논문 10편 · 필터 해제
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
We describe a minimalistic and interpretable method for unsupervised learning, without resorting to data augmentation, hyperparameter tuning, or other engineering designs, that achieves performance close to the SOTA SSL …
Self-Supervised LearningSparse Representation-based ClassificationSpectral Graph ClusteringUnsupervised Image Classification+1Improving Self-Organizing Maps with Unsupervised Feature Extraction
The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with …
ClassificationGeneral Classificationimage-classificationImage Classification+2Stacked Capsule Autoencoders
Objects are composed of a set of geometrically organized parts. We introduce an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships between parts to reason about objects. Since these re…
Cross-Modal RetrievalObjectUnsupervised MNISTInvariant Information Clustering for Unsupervised Image Classification and Segmentation
We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-th…
ClusteringGeneral Classificationimage-classificationImage Classification+5Inferencing Based on Unsupervised Learning of Disentangled Representations
Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way. We propose a framework that combines an…
DescriptiveRepresentation LearningUnsupervised Image ClassificationUnsupervised MNISTPixelGAN Autoencoders
In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and…
DecoderGenerative Adversarial NetworkUnsupervised Image ClassificationUnsupervised MNISTInfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adver…
Generative Adversarial NetworkImage GenerationRepresentation LearningUnsupervised Image Classification+1Ladder Variational Autoencoders
Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these …
Unsupervised MNISTUnsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information between observed exam…
ClusteringGeneral Classificationimage-classificationImage Classification+3Adversarial Autoencoders
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the a…
ClusteringData VisualizationDecoderDimensionality Reduction+4