41 research outputs found
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
We study the recently introduced stability training as a general-purpose
method to increase the robustness of deep neural networks against input
perturbations. In particular, we explore its use as an alternative to data
augmentation and validate its performance against a number of distortion types
and transformations including adversarial examples. In our image classification
experiments using ImageNet data stability training performs on a par or even
outperforms data augmentation for specific transformations, while consistently
offering improved robustness against a broader range of distortion strengths
and types unseen during training, a considerably smaller hyperparameter
dependence and less potentially negative side effects compared to data
augmentation.Comment: 18 pages, 25 figures; Camera-ready versio
Correlation length scalings in fusion edge plasma turbulence computations
The effect of changes in plasma parameters, that are characteristic near or
at an L-H transition in fusion edge plasmas, on fluctuation correlation lengths
are analysed by means of drift-Alfven turbulence computations. Scalings by
density gradient length, collisionality, plasma beta, and by an imposed shear
flow are considered. It is found that strongly sheared flows lead to the
appearence of long-range correlations in electrostatic potential fluctuations
parallel and perpendicular to the magnetic field.Comment: Submitted to "Plasma Physics and Controlled Fusion
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
Fusing data from multiple modalities provides more information to train
machine learning systems. However, it is prohibitively expensive and
time-consuming to label each modality with a large amount of data, which leads
to a crucial problem of semi-supervised multi-modal learning. Existing methods
suffer from either ineffective fusion across modalities or lack of theoretical
guarantees under proper assumptions. In this paper, we propose a novel
information-theoretic approach, namely \textbf{T}otal \textbf{C}orrelation
\textbf{G}ain \textbf{M}aximization (TCGM), for semi-supervised multi-modal
learning, which is endowed with promising properties: (i) it can utilize
effectively the information across different modalities of unlabeled data
points to facilitate training classifiers of each modality (ii) it has
theoretical guarantee to identify Bayesian classifiers, i.e., the ground truth
posteriors of all modalities. Specifically, by maximizing TC-induced loss
(namely TC gain) over classifiers of all modalities, these classifiers can
cooperatively discover the equivalent class of ground-truth classifiers; and
identify the unique ones by leveraging limited percentage of labeled data. We
apply our method to various tasks and achieve state-of-the-art results,
including news classification, emotion recognition and disease prediction.Comment: ECCV 2020 (oral