10,281 research outputs found
Manitest: Are classifiers really invariant?
Invariance to geometric transformations is a highly desirable property of
automatic classifiers in many image recognition tasks. Nevertheless, it is
unclear to which extent state-of-the-art classifiers are invariant to basic
transformations such as rotations and translations. This is mainly due to the
lack of general methods that properly measure such an invariance. In this
paper, we propose a rigorous and systematic approach for quantifying the
invariance to geometric transformations of any classifier. Our key idea is to
cast the problem of assessing a classifier's invariance as the computation of
geodesics along the manifold of transformed images. We propose the Manitest
method, built on the efficient Fast Marching algorithm to compute the
invariance of classifiers. Our new method quantifies in particular the
importance of data augmentation for learning invariance from data, and the
increased invariance of convolutional neural networks with depth. We foresee
that the proposed generic tool for measuring invariance to a large class of
geometric transformations and arbitrary classifiers will have many applications
for evaluating and comparing classifiers based on their invariance, and help
improving the invariance of existing classifiers.Comment: BMVC 201
Coupled Depth Learning
In this paper we propose a method for estimating depth from a single image
using a coarse to fine approach. We argue that modeling the fine depth details
is easier after a coarse depth map has been computed. We express a global
(coarse) depth map of an image as a linear combination of a depth basis learned
from training examples. The depth basis captures spatial and statistical
regularities and reduces the problem of global depth estimation to the task of
predicting the input-specific coefficients in the linear combination. This is
formulated as a regression problem from a holistic representation of the image.
Crucially, the depth basis and the regression function are {\bf coupled} and
jointly optimized by our learning scheme. We demonstrate that this results in a
significant improvement in accuracy compared to direct regression of depth
pixel values or approaches learning the depth basis disjointly from the
regression function. The global depth estimate is then used as a guidance by a
local refinement method that introduces depth details that were not captured at
the global level. Experiments on the NYUv2 and KITTI datasets show that our
method outperforms the existing state-of-the-art at a considerably lower
computational cost for both training and testing.Comment: 10 pages, 3 Figures, 4 Tables with quantitative evaluation
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