Deterministic variational inference for robust Bayesian neural networks

Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches

Bayesian neural network priors at the level of units

International audienceWe investigate deep Bayesian neural networks with Gaussian priors on the weights and ReLU-like nonlinearities, shedding light on novel sparsity-inducing mechanisms at the level of the units of the network. Bayesian neural networks with Gaussian priors are well known to induce the weight decay penalty on the weights. In contrast, our result indicates a more elaborate regularization scheme at the level of the units, ranging from convex penalties for the first two layers-L 2 regularization for the first and Lasso for the second-to non convex penalties for deeper layers. Thus, although weight decay does not allow for the weights to be set exactly to zero, sparse solutions tend to be selected for the units from the second layer onward. This result provides new theoretical insight on deep Bayesian neural networks, underpinning their natural shrinkage properties and practical potential

Towards Analyzing Semantic Robustness of Deep Neural Networks

Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pose). We propose a theoretically grounded analysis for DNN robustness in the semantic space. We qualitatively analyze different DNNs' semantic robustness by visualizing the DNN global behavior as semantic maps and observe interesting behavior of some DNNs. Since generating these semantic maps does not scale well with the dimensionality of the semantic space, we develop a bottom-up approach to detect robust regions of DNNs. To achieve this, we formalize the problem of finding robust semantic regions of the network as optimizing integral bounds and we develop expressions for update directions of the region bounds. We use our developed formulations to quantitatively evaluate the semantic robustness of different popular network architectures. We show through extensive experimentation that several networks, while trained on the same dataset and enjoying comparable accuracy, do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust than ResNet50. We hope that this tool will serve as a milestone towards understanding the semantic robustness of DNNs.Comment: Presented at European conference on computer vision (ECCV 2020) Workshop on Adversarial Robustness in the Real World ( https://eccv20-adv-workshop.github.io/ ) [best paper award]. The code is available at https://github.com/ajhamdi/semantic-robustnes

Robust Visual SLAM in Challenging Environments with Low-texture and Dynamic Illumination

- Robustness to Dynamic Illumination conditions is also one of the main open challenges in visual odometry and SLAM, e.g. high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors, for instance automatic settings of a camera might not react fast enough to properly record dynamic illumination changes, and also from limitations in the algorithms, e.g. the track of interest points is typically based on brightness constancy. The work of this thesis contributes to mitigate these phenomena from two different perspectives. The first one addresses this problem from a deep learning perspective by enhancing images to invariant and richer representations for VO and SLAM, benefiting from the generalization properties of deep neural networks. In this work it is also demonstrated how the insertion of long short term memory (LSTM) allows us to obtain temporally consistent sequences, since the estimation depends on previous states. Secondly, a more traditional perspective is exploited to contribute with a purely geometric-based tracking of line segments in challenging stereo streams with complex or varying illumination, since they are intrinsically more informative. Fecha de lectura de Tesis Doctoral: 26 de febrero 2020In the last years, visual Simultaneous Localization and Mapping (SLAM) has played a role of capital importance in rapid technological advances, e.g. mo- bile robotics and applications such as virtual, augmented, or mixed reality (VR/AR/MR), as a vital part of their processing pipelines. As its name indicates, it comprises the estimation of the state of a robot (typically the pose) while, simultaneously, incrementally building and refining a consistent representation of the environment, i.e. the so-called map, based on the equipped sensors. Despite the maturity reached by state-of-art visual SLAM techniques in controlled environments, there are still many open challenges to address be- fore reaching a SLAM system robust to long-term operations in uncontrolled scenarios, where classical assumptions, such as static environments, do not hold anymore. This thesis contributes to improve robustness of visual SLAM in harsh or difficult environments, in particular: - Low-textured Environments, where traditional approaches suffer from an accuracy impoverishment and, occasionally, the absolute failure of the system. Fortunately, many of such low-textured environments contain planar elements that are rich in linear shapes, so an alternative feature choice such as line segments would exploit information from structured parts of the scene. This set of contributions exploits both type of features, i.e. points and line segments, to produce visual odometry and SLAM algorithms robust in a broader variety of environments, hence leveraging them at all instances of the related processes: monocular depth estimation, visual odometry, keyframe selection, bundle adjustment, loop closing, etc. Additionally, an open-source C++ implementation of the proposed algorithms has been released along with the published articles and some extra multimedia material for the benefit of the community