22 research outputs found
Localization Uncertainty Estimation for Anchor-Free Object Detection
Since many safety-critical systems, such as surgical robots and autonomous
driving cars, are in unstable environments with sensor noise and incomplete
data, it is desirable for object detectors to take into account the confidence
of localization prediction. There are three limitations of the prior
uncertainty estimation methods for anchor-based object detection. 1) They model
the uncertainty based on object properties having different characteristics,
such as location (center point) and scale (width, height). 2) they model a box
offset and ground-truth as Gaussian distribution and Dirac delta distribution,
which leads to the model misspecification problem. Because the Dirac delta
distribution is not exactly represented as Gaussian, i.e., for any and
. 3) Since anchor-based methods are sensitive to hyper-parameters of
anchor, the localization uncertainty modeling is also sensitive to these
parameters. Therefore, we propose a new localization uncertainty estimation
method called Gaussian-FCOS for anchor-free object detection. Our method
captures the uncertainty based on four directions of box offsets~(left, right,
top, bottom) that have similar properties, which enables to capture which
direction is uncertain and provide a quantitative value in range~[0, 1]. To
this end, we design a new uncertainty loss, negative power log-likelihood loss,
to measure uncertainty by weighting IoU to the likelihood loss, which
alleviates the model misspecification problem. Experiments on COCO datasets
demonstrate that our Gaussian-FCOS reduces false positives and finds more
missing-objects by mitigating over-confidence scores with the estimated
uncertainty. We hope Gaussian-FCOS serves as a crucial component for the
reliability-required task
Bayesian Augmentation of Deep Learning to Improve Video Classification
Traditional automated video classification methods lack measures of uncertainty, meaning the network is unable to identify those cases in which its predictions are made with significant uncertainty. This leads to misclassification, as the traditional network classifies each observation with same amount of certainty, no matter what the observation is. Bayesian neural networks are a remedy to this issue by leveraging Bayesian inference to construct uncertainty measures for each prediction. Because exact Bayesian inference is typically intractable due to the large number of parameters in a neural network, Bayesian inference is approximated by utilizing dropout in a convolutional neural network. This research compared a traditional video classification neural network to its Bayesian equivalent based on performance and capabilities. The Bayesian network achieves higher accuracy than a comparable non-Bayesian video network and it further provides uncertainty measures for each classification
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Bayesian Neural Networks (BNNs) are trained to optimize an entire
distribution over their weights instead of a single set, having significant
advantages in terms of, e.g., interpretability, multi-task learning, and
calibration. Because of the intractability of the resulting optimization
problem, most BNNs are either sampled through Monte Carlo methods, or trained
by minimizing a suitable Evidence Lower BOund (ELBO) on a variational
approximation. In this paper, we propose a variant of the latter, wherein we
replace the Kullback-Leibler divergence in the ELBO term with a Maximum Mean
Discrepancy (MMD) estimator, inspired by recent work in variational inference.
After motivating our proposal based on the properties of the MMD term, we
proceed to show a number of empirical advantages of the proposed formulation
over the state-of-the-art. In particular, our BNNs achieve higher accuracy on
multiple benchmarks, including several image classification tasks. In addition,
they are more robust to the selection of a prior over the weights, and they are
better calibrated. As a second contribution, we provide a new formulation for
estimating the uncertainty on a given prediction, showing it performs in a more
robust fashion against adversarial attacks and the injection of noise over
their inputs, compared to more classical criteria such as the differential
entropy