66 research outputs found
Data-free parameter pruning for Deep Neural Networks
Deep Neural nets (NNs) with millions of parameters are at the heart of many
state-of-the-art computer vision systems today. However, recent works have
shown that much smaller models can achieve similar levels of performance. In
this work, we address the problem of pruning parameters in a trained NN model.
Instead of removing individual weights one at a time as done in previous works,
we remove one neuron at a time. We show how similar neurons are redundant, and
propose a systematic way to remove them. Our experiments in pruning the densely
connected layers show that we can remove upto 85\% of the total parameters in
an MNIST-trained network, and about 35\% for AlexNet without significantly
affecting performance. Our method can be applied on top of most networks with a
fully connected layer to give a smaller network.Comment: BMVC 201
Compensating for Large In-Plane Rotations in Natural Images
Rotation invariance has been studied in the computer vision community
primarily in the context of small in-plane rotations. This is usually achieved
by building invariant image features. However, the problem of achieving
invariance for large rotation angles remains largely unexplored. In this work,
we tackle this problem by directly compensating for large rotations, as opposed
to building invariant features. This is inspired by the neuro-scientific
concept of mental rotation, which humans use to compare pairs of rotated
objects. Our contributions here are three-fold. First, we train a Convolutional
Neural Network (CNN) to detect image rotations. We find that generic CNN
architectures are not suitable for this purpose. To this end, we introduce a
convolutional template layer, which learns representations for canonical
'unrotated' images. Second, we use Bayesian Optimization to quickly sift
through a large number of candidate images to find the canonical 'unrotated'
image. Third, we use this method to achieve robustness to large angles in an
image retrieval scenario. Our method is task-agnostic, and can be used as a
pre-processing step in any computer vision system.Comment: Accepted at Indian Conference on Computer Vision, Graphics and Image
Processing (ICVGIP) 201
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
Genetic dissection of itpr gene function reveals a vital requirement in aminergic cells of Drosophila larvae
Signaling by the second messenger inositol 1,4,5-trisphosphate is thought to affect several developmental and physiological processes. Mutants in the inositol 1,4,5-trisphosphate receptor (itpr) gene of Drosophila exhibit delays in molting while stronger alleles are also larval lethal. In a freshly generated set of EMS alleles for the itpr locus we have sequenced and identified single point mutations in seven mutant chromosomes. The predicted allelic strength of these mutants matches the observed levels of lethality. They range from weak hypomorphs to complete nulls. Interestingly, lethality in three heteroallelic combinations has a component of cold sensitivity. The temporal focus of cold sensitivity lies in the larval stages, predominantly at second instar. Coupled with our earlier observation that an itpr homozygous null allele dies at the second instar stage, it appears that there is a critical period for itpr gene function in second instar larvae. Here we show that the focus of this critical function lies in aminergic cells by rescue with UAS-itpr and DdCGAL4. However, this function does not require synaptic activity, suggesting that InsP3-mediated Ca2+ release regulates the neurohormonal action of serotonin
Infantile systemic hyalinosis: A case report
Infantile systemic hyalinosis (ISH) is a rare, progressive, autosomal recessive disorder characterized by connective tissue involvementas hyaline deposition in the skin, gastrointestinal tract, muscles, glands, and other organs. We report a child with this rare conditionpresenting with growth retardation, joint contractures, and intractable diarrhea. Though genetically analyzed, ISH still remains as apoorly understood disease raising concerns during diagnosis and treatment
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