81 research outputs found
Decision Stream: Cultivating Deep Decision Trees
Various modifications of decision trees have been extensively used during the
past years due to their high efficiency and interpretability. Tree node
splitting based on relevant feature selection is a key step of decision tree
learning, at the same time being their major shortcoming: the recursive nodes
partitioning leads to geometric reduction of data quantity in the leaf nodes,
which causes an excessive model complexity and data overfitting. In this paper,
we present a novel architecture - a Decision Stream, - aimed to overcome this
problem. Instead of building a tree structure during the learning process, we
propose merging nodes from different branches based on their similarity that is
estimated with two-sample test statistics, which leads to generation of a deep
directed acyclic graph of decision rules that can consist of hundreds of
levels. To evaluate the proposed solution, we test it on several common machine
learning problems - credit scoring, twitter sentiment analysis, aircraft flight
control, MNIST and CIFAR image classification, synthetic data classification
and regression. Our experimental results reveal that the proposed approach
significantly outperforms the standard decision tree learning methods on both
regression and classification tasks, yielding a prediction error decrease up to
35%
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Despite a rapid rise in the quality of built-in smartphone cameras, their
physical limitations - small sensor size, compact lenses and the lack of
specific hardware, - impede them to achieve the quality results of DSLR
cameras. In this work we present an end-to-end deep learning approach that
bridges this gap by translating ordinary photos into DSLR-quality images. We
propose learning the translation function using a residual convolutional neural
network that improves both color rendition and image sharpness. Since the
standard mean squared loss is not well suited for measuring perceptual image
quality, we introduce a composite perceptual error function that combines
content, color and texture losses. The first two losses are defined
analytically, while the texture loss is learned in an adversarial fashion. We
also present DPED, a large-scale dataset that consists of real photos captured
from three different phones and one high-end reflex camera. Our quantitative
and qualitative assessments reveal that the enhanced image quality is
comparable to that of DSLR-taken photos, while the methodology is generalized
to any type of digital camera
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Low-end and compact mobile cameras demonstrate limited photo quality mainly
due to space, hardware and budget constraints. In this work, we propose a deep
learning solution that translates photos taken by cameras with limited
capabilities into DSLR-quality photos automatically. We tackle this problem by
introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image
Generative Adversarial Network-based architecture. The proposed model is
trained by under weak supervision: unlike previous works, there is no need for
strong supervision in the form of a large annotated dataset of aligned
original/enhanced photo pairs. The sole requirement is two distinct datasets:
one from the source camera, and one composed of arbitrary high-quality images
that can be generally crawled from the Internet - the visual content they
exhibit may be unrelated. Hence, our solution is repeatable for any camera:
collecting the data and training can be achieved in a couple of hours. In this
work, we emphasize on extensive evaluation of obtained results. Besides
standard objective metrics and subjective user study, we train a virtual rater
in the form of a separate CNN that mimics human raters on Flickr data and use
this network to get reference scores for both original and enhanced photos. Our
experiments on the DPED, KITTI and Cityscapes datasets as well as pictures from
several generations of smartphones demonstrate that WESPE produces comparable
or improved qualitative results with state-of-the-art strongly supervised
methods
Multiple coupling of silanes with imido complexes of Mo
The bis(imido) complexes (tBuNv)2Mo(PMe3)(L) (L = PMe3, C2H4) react with up to three equivalents of
silane PhSiH3 to give the imido-bridged disilyl silyl Mo(VI) complex (tBuN){μ-tBuN(SiHPh)2}Mo(H)(SiH2Ph)-
(PMe3)2 (3) studied by NMR, IR and X-ray diffraction. NMR data supported by DFT calculations show that
complex 3 is an unusual example of a silyl hydride of Mo(VI), without significant Si⋯H interaction. Mechanistic
NMR studies revealed that silane addition proceeds in a stepwise manner via a series of Si–H⋯M
agostic and silanimine complexes whose structures were further elucidated by DFT calculation
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