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