paper-with-me

홈 › Papers

A Generative Model for Deep Convolutional Learning

2015-04-15 · Yunchen Pu, Xin Yuan, Lawrence Carin

A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images, and excellent classification results are obtained on the MNIST and Caltech 101 datasets.

📄 PDF Abstract BibTeX arXiv:1504.04054

Code (0)

등록된 구현이 없습니다.

Tasks

Dictionary LearningGeneral Classificationmodel

Similar Papers 제목 키워드 기반

AM-DCGAN: Analog Memristive Hardware Accelerator for Deep Convolutional Generative Adversarial Networks

2020-06-20 · Olga Krestinskaya, Bhaskar Choubey, Alex Pappachen James

Generative Adversarial Network (GAN) is a well known computationally complex algorithm requiring signficiant computational resources in software implementations including large amount of data to be trained. This makes it…

Generative Adversarial Network

CapsuleGAN: Generative Adversarial Capsule Network

2018-02-17 · Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan

We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adver…

General ClassificationGenerative Adversarial Networkimage-classificationImage Classification+1

Invertibility of Convolutional Generative Networks from Partial Measurements

2018-12-01 · NeurIPS 2018 12 · Fangchang Ma, Ulas Ayaz, Sertac Karaman

In this work, we present new theoretical results on convolutional generative neural networks, in particular their invertibility (i.e., the recovery of input latent code given the network output). The study of network inv…

Image Inpainting

Generative Models for Stochastic Processes Using Convolutional Neural Networks

2018-01-09 · Fernando Fernandes Neto

The present paper aims to demonstrate the usage of Convolutional Neural Networks as a generative model for stochastic processes, enabling researchers from a wide range of fields (such as quantitative finance and physics)…

Generative Deep Deconvolutional Learning

2014-12-18 · Yunchen Pu, Xin Yuan, Lawrence Carin

A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up and top-down prob…

Dictionary LearningGeneral Classification