11 research outputs found
Smoothed Embeddings for Certified Few-Shot Learning
Randomized smoothing is considered to be the state-of-the-art provable
defense against adversarial perturbations. However, it heavily exploits the
fact that classifiers map input objects to class probabilities and do not focus
on the ones that learn a metric space in which classification is performed by
computing distances to embeddings of classes prototypes. In this work, we
extend randomized smoothing to few-shot learning models that map inputs to
normalized embeddings. We provide analysis of Lipschitz continuity of such
models and derive robustness certificate against -bounded perturbations
that may be useful in few-shot learning scenarios. Our theoretical results are
confirmed by experiments on different datasets
Real-world adversarial attack on MTCNN face detection system
Recent studies proved that deep learning approaches achieve remarkable
results on face detection task. On the other hand, the advances gave rise to a
new problem associated with the security of the deep convolutional neural
network models unveiling potential risks of DCNNs based applications. Even
minor input changes in the digital domain can result in the network being
fooled. It was shown then that some deep learning-based face detectors are
prone to adversarial attacks not only in a digital domain but also in the real
world. In the paper, we investigate the security of the well-known cascade CNN
face detection system - MTCNN and introduce an easily reproducible and a robust
way to attack it. We propose different face attributes printed on an ordinary
white and black printer and attached either to the medical face mask or to the
face directly. Our approach is capable of breaking the MTCNN detector in a
real-world scenario
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
This paper proposes a fast and scalable method for uncertainty quantification
of machine learning models' predictions. First, we show the principled way to
measure the uncertainty of predictions for a classifier based on
Nadaraya-Watson's nonparametric estimate of the conditional label distribution.
Importantly, the proposed approach allows to disentangle explicitly aleatoric
and epistemic uncertainties. The resulting method works directly in the feature
space. However, one can apply it to any neural network by considering an
embedding of the data induced by the network. We demonstrate the strong
performance of the method in uncertainty estimation tasks on text
classification problems and a variety of real-world image datasets, such as
MNIST, SVHN, CIFAR-100 and several versions of ImageNet.Comment: NeurIPS 2022 pape
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture
Supporting the current trend in the AI community, we present the AI Journey
2021 Challenge called Fusion Brain, the first competition which is targeted to
make the universal architecture which could process different modalities (in
this case, images, texts, and code) and solve multiple tasks for vision and
language. The Fusion Brain Challenge combines the following specific tasks:
Code2code Translation, Handwritten Text recognition, Zero-shot Object
Detection, and Visual Question Answering. We have created datasets for each
task to test the participants' submissions on it. Moreover, we have collected
and made publicly available a new handwritten dataset in both English and
Russian, which consists of 94,128 pairs of images and texts. We also propose a
multimodal and multitask architecture - a baseline solution, in the center of
which is a frozen foundation model and which has been trained in Fusion mode
along with Single-task mode. The proposed Fusion approach proves to be
competitive and more energy-efficient compared to the task-specific one