Aug 23, 2020 · In this work, we propose Dual Adversarial Auto-encoder (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information ...
Jun 20, 2019 · In this brief, we propose dual AAE (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information between observed ...
In this work, we propose Dual Adversarial. Auto-encoder (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information between ...
This work proposes dual AAE (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information between observed examples.
This repository provides a PyTorch implementation of the paper Dual Adversarial Autoencoders for Clustering. Dependencies. Python 3.5. Pytorch 0.2.0. Usage. 1 ...
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In this work, we propose Dual Adversarial Auto-encoder (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information between observed ...
Mar 15, 2024 · Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.
In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering. We first devise a dual autoencoder network, which ...
Apr 1, 2020 · Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods. In addition, by adding ...
In order to guide the optimizing procedure of the dual autoencoder network, a self-supervised cluster prediction module is designed to predict clustering ...