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Explainable and Advisable Learning for Self-driving Vehicles
Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Over the last decade, Convolutional Neural Network (CNN) models have been
highly successful in solving complex vision problems. However, these deep
models are perceived as "black box" methods considering the lack of
understanding of their internal functioning. There has been a significant
recent interest in developing explainable deep learning models, and this paper
is an effort in this direction. Building on a recently proposed method called
Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide
better visual explanations of CNN model predictions, in terms of better object
localization as well as explaining occurrences of multiple object instances in
a single image, when compared to state-of-the-art. We provide a mathematical
derivation for the proposed method, which uses a weighted combination of the
positive partial derivatives of the last convolutional layer feature maps with
respect to a specific class score as weights to generate a visual explanation
for the corresponding class label. Our extensive experiments and evaluations,
both subjective and objective, on standard datasets showed that Grad-CAM++
provides promising human-interpretable visual explanations for a given CNN
architecture across multiple tasks including classification, image caption
generation and 3D action recognition; as well as in new settings such as
knowledge distillation.Comment: 17 Pages, 15 Figures, 11 Tables. Accepted in the proceedings of IEEE
Winter Conf. on Applications of Computer Vision (WACV2018). Extended version
is under review at IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Why do These Match? Explaining the Behavior of Image Similarity Models
Explaining a deep learning model can help users understand its behavior and
allow researchers to discern its shortcomings. Recent work has primarily
focused on explaining models for tasks like image classification or visual
question answering. In this paper, we introduce Salient Attributes for Network
Explanation (SANE) to explain image similarity models, where a model's output
is a score measuring the similarity of two inputs rather than a classification
score. In this task, an explanation depends on both of the input images, so
standard methods do not apply. Our SANE explanations pairs a saliency map
identifying important image regions with an attribute that best explains the
match. We find that our explanations provide additional information not
typically captured by saliency maps alone, and can also improve performance on
the classic task of attribute recognition. Our approach's ability to generalize
is demonstrated on two datasets from diverse domains, Polyvore Outfits and
Animals with Attributes 2. Code available at:
https://github.com/VisionLearningGroup/SANEComment: Accepted at ECCV 202
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