Targeted Deep Learning: Framework, Methods, and Applications

05/28/2021
by   Shih-Ting Huang, et al.
0

Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic situations for which they were not specifically trained. In general, the ability to cope with a broad spectrum of unseen test inputs is called generalization. Generalization is definitely important in applications where the possible test inputs are known but plentiful or simply unknown, but there are also cases where the possible inputs are few and unlabeled but known beforehand. For example, medicine is currently interested in targeting treatments to individual patients; the number of patients at any given time is usually small (typically one), their diagnoses/responses/... are still unknown, but their general characteristics (such as genome information, protein levels in the blood, and so forth) are known before the treatment. We propose to call deep learning in such applications targeted deep learning. In this paper, we introduce a framework for targeted deep learning, and we devise and test an approach for adapting standard pipelines to the requirements of targeted deep learning. The approach is very general yet easy to use: it can be implemented as a simple data-preprocessing step. We demonstrate on a variety of real-world data that our approach can indeed render standard deep learning faster and more accurate when the test inputs are known beforehand.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

12/02/2018

Model-Reuse Attacks on Deep Learning Systems

Many of today's machine learning (ML) systems are built by reusing an ar...
05/05/2019

Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples

A large body of recent work has investigated the phenomenon of evasion a...
05/15/2021

An Effective Baseline for Robustness to Distributional Shift

Refraining from confidently predicting when faced with categories of inp...
11/18/2018

Deep Learning with Inaccurate Training Data for Image Restoration

In many applications of deep learning, particularly those in image resto...
01/08/2018

Solutions to problems with deep learning

Despite the several successes of deep learning systems, there are concer...
11/16/2020

Learning from Task Descriptions

Typically, machine learning systems solve new tasks by training on thous...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.