7,622 research outputs found
"Mental Rotation" by Optimizing Transforming Distance
The human visual system is able to recognize objects despite transformations
that can drastically alter their appearance. To this end, much effort has been
devoted to the invariance properties of recognition systems. Invariance can be
engineered (e.g. convolutional nets), or learned from data explicitly (e.g.
temporal coherence) or implicitly (e.g. by data augmentation). One idea that
has not, to date, been explored is the integration of latent variables which
permit a search over a learned space of transformations. Motivated by evidence
that people mentally simulate transformations in space while comparing
examples, so-called "mental rotation", we propose a transforming distance.
Here, a trained relational model actively transforms pairs of examples so that
they are maximally similar in some feature space yet respect the learned
transformational constraints. We apply our method to nearest-neighbour problems
on the Toronto Face Database and NORB
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an
approach to visualize and understand the decisions made by deep neural networks
(DNNs) given a specific input. CLEAR facilitates the visualization of attentive
regions and levels of interest of DNNs during the decision-making process. It
also enables the visualization of the most dominant classes associated with
these attentive regions of interest. As such, CLEAR can mitigate some of the
shortcomings of heatmap-based methods associated with decision ambiguity, and
allows for better insights into the decision-making process of DNNs.
Quantitative and qualitative experiments across three different datasets
demonstrate the efficacy of CLEAR for gaining a better understanding of the
inner workings of DNNs during the decision-making process.Comment: Accepted at Computer Vision and Patter Recognition Workshop (CVPR-W)
on Explainable Computer Vision, 201
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