4 research outputs found
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs
Deep neural networks are often not robust to semantically-irrelevant changes
in the input. In this work we address the issue of robustness of
state-of-the-art deep convolutional neural networks (CNNs) against commonly
occurring distortions in the input such as photometric changes, or the addition
of blur and noise. These changes in the input are often accounted for during
training in the form of data augmentation. We have two major contributions:
First, we propose a new regularization loss called feature-map augmentation
(FMA) loss which can be used during finetuning to make a model robust to
several distortions in the input. Second, we propose a new combined
augmentations (CA) finetuning strategy, that results in a single model that is
robust to several augmentation types at the same time in a data-efficient
manner. We use the CA strategy to improve an existing state-of-the-art method
called stability training (ST). Using CA, on an image classification task with
distorted images, we achieve an accuracy improvement of on average 8.94% with
FMA and 8.86% with ST absolute on CIFAR-10 and 8.04% with FMA and 8.27% with ST
absolute on ImageNet, compared to 1.98% and 2.12%, respectively, with the well
known data augmentation method, while keeping the clean baseline performance.Comment: Accepted at ACM CSCS 2020 (8 pages, 4 figures
Invariance Measures for Neural Networks
Invariances in neural networks are useful and necessary for many tasks.
However, the representation of the invariance of most neural network models has
not been characterized. We propose measures to quantify the invariance of
neural networks in terms of their internal representation. The measures are
efficient and interpretable, and can be applied to any neural network model.
They are also more sensitive to invariance than previously defined measures. We
validate the measures and their properties in the domain of affine
transformations and the CIFAR10 and MNIST datasets, including their stability
and interpretability. Using the measures, we perform a first analysis of CNN
models and show that their internal invariance is remarkably stable to random
weight initializations, but not to changes in dataset or transformation. We
believe the measures will enable new avenues of research in invariance
representation