Mohammad Havaei

Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay

Xiang Jiang

Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task … (voir plus)drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.

2020-03-08

ArXiv (prépublication)

arxiv.org

InfoMask: Masked Variational Latent Representation to Localize Chest Disease

Saeid Asgari Taghanaki

Mohammad Havaei

Tess Berthier

Francis Dutil

Lisa Di Jorio

Ghassan Hamarneh

Yoshua Bengio

2019-10-09

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org

Information Fusion in Deep Convolutional Neural Networks for Biomedical Image Segmentation 1

Mohammad Havaei

Nicolas Guizard

Nicolas Chapados

Yoshua Bengio

2018-07-03

Signal Processing and Machine Learning for Biomedical Big Data (publié)

doi.org

Learnable Explicit Density for Continuous Latent Space and Variational Inference

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its correspon… (voir plus)ding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample generation and inference. Second, we analyze the family of inverse autoregressive flows (inverse AF) and show that with further improvement, inverse AF could be used as universal approximation to any complicated posterior. Our analysis results in a unified approach to parameterizing a VAE, without the need to restrict ourselves to use factorial Gaussians in the latent real space.

2017-10-05

ArXiv (prépublication)

arxiv.org