Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
Name
CBMM-Memo-119.pdf
Size
31.08 MB
Format
Adobe PDF
Checksum (MD5)
d2322ceb1ac64e2490fa3e5ec4f0d305
Author(s)
Sakai, Akira
Sunagawa, Taro
Madan, Spandan
Suzuki, Kanata
Katoh, Takashi
Kobashi, Hiromichi
Pfister, Hanspeter
Sinha, Pawan
Boix, Xavier
Sasaki, Tomotake
Date Issued
January 26, 2022
Publisher
Center for Brains, Minds and Machines (CBMM)
Series/Report no.
CBMM Memo;119
Abstract
The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illu- minations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available. In this paper, we investigate three different approaches to improve DNNs in recognizing objects in OoD orientations and illuminations. Namely, these are (i) training much longer after convergence of the in-distribution (InD) validation accuracy, i.e., late-stopping, (ii) tuning the momentum parameter of the batch normalization layers, and (iii) enforcing invariance of the neural activity in an intermediate layer to orientation and illumination conditions. Each of these approaches substantially improves the DNN’s OoD accuracy (more than 20% in some cases). We report results in four datasets: two datasets are modified from the MNIST and iLab datasets, and the other two are novel (one of 3D rendered cars and another of objects taken from various controlled orientations and illumination conditions). These datasets allow to study the effects of different amounts of bias and are challenging as DNNs perform poorly in OoD conditions. Finally, we demonstrate that even though the three approaches focus on different aspects of DNNs, they all tend to lead to the same underlying neural mechanism to enable OoD accuracy gains – individual neurons in the intermediate layers become more selective to a category and also invariant to OoD orientations and illumina- tions. We anticipate this study to be a basis for further improvement of deep neural networks’ OoD generalization performance, which is highly demanded to achieve safe and fair AI applications.
Subjects
Out-of-distribution Generalization
Object Recognition in Novel Conditions
Neural Invariance
Neural Selectivity
Neural Activity Analysis
Persistent DSpace Link
