13 research outputs found
An Empirical Evaluation of Visual Question Answering for Novel Objects
We study the problem of answering questions about images in the harder
setting, where the test questions and corresponding images contain novel
objects, which were not queried about in the training data. Such setting is
inevitable in real world-owing to the heavy tailed distribution of the visual
categories, there would be some objects which would not be annotated in the
train set. We show that the performance of two popular existing methods drop
significantly (up to 28%) when evaluated on novel objects cf. known objects. We
propose methods which use large existing external corpora of (i) unlabeled
text, i.e. books, and (ii) images tagged with classes, to achieve novel object
based visual question answering. We do systematic empirical studies, for both
an oracle case where the novel objects are known textually, as well as a fully
automatic case without any explicit knowledge of the novel objects, but with
the minimal assumption that the novel objects are semantically related to the
existing objects in training. The proposed methods for novel object based
visual question answering are modular and can potentially be used with many
visual question answering architectures. We show consistent improvements with
the two popular architectures and give qualitative analysis of the cases where
the model does well and of those where it fails to bring improvements.Comment: 11 pages, 4 figures, accepted in CVPR 2017 (poster
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
The susceptibility of modern machine learning classifiers to adversarial
examples has motivated theoretical results suggesting that these might be
unavoidable. However, these results can be too general to be applicable to
natural data distributions. Indeed, humans are quite robust for tasks involving
vision. This apparent conflict motivates a deeper dive into the question: Are
adversarial examples truly unavoidable? In this work, we theoretically
demonstrate that a key property of the data distribution -- concentration on
small-volume subsets of the input space -- determines whether a robust
classifier exists. We further demonstrate that, for a data distribution
concentrated on a union of low-dimensional linear subspaces, exploiting data
structure naturally leads to classifiers that enjoy good robustness guarantees,
improving upon methods for provable certification in certain regimes.Comment: Accepted to Neural Information Processing Systems (NeurIPS) 202
Physics Potential of the ICAL detector at the India-based Neutrino Observatory (INO)
The upcoming 50 kt magnetized iron calorimeter (ICAL) detector at the
India-based Neutrino Observatory (INO) is designed to study the atmospheric
neutrinos and antineutrinos separately over a wide range of energies and path
lengths. The primary focus of this experiment is to explore the Earth matter
effects by observing the energy and zenith angle dependence of the atmospheric
neutrinos in the multi-GeV range. This study will be crucial to address some of
the outstanding issues in neutrino oscillation physics, including the
fundamental issue of neutrino mass hierarchy. In this document, we present the
physics potential of the detector as obtained from realistic detector
simulations. We describe the simulation framework, the neutrino interactions in
the detector, and the expected response of the detector to particles traversing
it. The ICAL detector can determine the energy and direction of the muons to a
high precision, and in addition, its sensitivity to multi-GeV hadrons increases
its physics reach substantially. Its charge identification capability, and
hence its ability to distinguish neutrinos from antineutrinos, makes it an
efficient detector for determining the neutrino mass hierarchy. In this report,
we outline the analyses carried out for the determination of neutrino mass
hierarchy and precision measurements of atmospheric neutrino mixing parameters
at ICAL, and give the expected physics reach of the detector with 10 years of
runtime. We also explore the potential of ICAL for probing new physics
scenarios like CPT violation and the presence of magnetic monopoles.Comment: 139 pages, Physics White Paper of the ICAL (INO) Collaboration,
Contents identical with the version published in Pramana - J. Physic
Understanding Noise-Augmented Training for Randomized Smoothing
Randomized smoothing is a technique for providing provable robustness
guarantees against adversarial attacks while making minimal assumptions about a
classifier. This method relies on taking a majority vote of any base classifier
over multiple noise-perturbed inputs to obtain a smoothed classifier, and it
remains the tool of choice to certify deep and complex neural network models.
Nonetheless, non-trivial performance of such smoothed classifier crucially
depends on the base model being trained on noise-augmented data, i.e., on a
smoothed input distribution. While widely adopted in practice, it is still
unclear how this noisy training of the base classifier precisely affects the
risk of the robust smoothed classifier, leading to heuristics and tricks that
are poorly understood. In this work we analyze these trade-offs theoretically
in a binary classification setting, proving that these common observations are
not universal. We show that, without making stronger distributional
assumptions, no benefit can be expected from predictors trained with
noise-augmentation, and we further characterize distributions where such
benefit is obtained. Our analysis has direct implications to the practical
deployment of randomized smoothing, and we illustrate some of these via
experiments on CIFAR-10 and MNIST, as well as on synthetic datasets.Comment: Transactions on Machine Learning Research, 202