DeepAI AI Chat
Log In Sign Up

DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout

03/19/2016
by   Abhijit Guha Roy, et al.
ERNET India
0

Domain adaptation deals with adapting behaviour of machine learning based systems trained using samples in source domain to their deployment in target domain where the statistics of samples in both domains are dissimilar. The task of directly training or adapting a learner in the target domain is challenged by lack of abundant labeled samples. In this paper we propose a technique for domain adaptation in stacked autoencoder (SAE) based deep neural networks (DNN) performed in two stages: (i) unsupervised weight adaptation using systematic dropouts in mini-batch training, (ii) supervised fine-tuning with limited number of labeled samples in target domain. We experimentally evaluate performance in the problem of retinal vessel segmentation where the SAE-DNN is trained using large number of labeled samples in the source domain (DRIVE dataset) and adapted using less number of labeled samples in target domain (STARE dataset). The performance of SAE-DNN measured using logloss in source domain is 0.19, without and with adaptation are 0.40 and 0.18, and 0.39 when trained exclusively with limited samples in target domain. The area under ROC curve is observed respectively as 0.90, 0.86, 0.92 and 0.87. The high efficiency of vessel segmentation with DASA strongly substantiates our claim.

READ FULL TEXT

page 2

page 5

07/01/2016

Domain Adaptation for Neural Networks by Parameter Augmentation

We propose a simple domain adaptation method for neural networks in a su...
08/02/2021

Domain Adaptation for Autoencoder-Based End-to-End Communication Over Wireless Channels

The problem of domain adaptation conventionally considers the setting wh...
12/19/2022

Source-Free Domain Adaptation for Question Answering with Masked Self-training

Most previous unsupervised domain adaptation (UDA) methods for question ...
05/10/2023

Best-Effort Adaptation

We study a problem of best-effort adaptation motivated by several applic...
09/11/2023

Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation

The high acquisition cost and the significant demand for disruptive disc...
08/01/2023

Domain Adaptation based on Human Feedback for Enhancing Generative Model Denoising Abilities

How can we apply human feedback into generative model? As answer of this...