5,900 research outputs found
Gradient-free activation maximization for identifying effective stimuli
A fundamental question for understanding brain function is what types of
stimuli drive neurons to fire. In visual neuroscience, this question has also
been posted as characterizing the receptive field of a neuron. The search for
effective stimuli has traditionally been based on a combination of insights
from previous studies, intuition, and luck. Recently, the same question has
emerged in the study of units in convolutional neural networks (ConvNets), and
together with this question a family of solutions were developed that are
generally referred to as "feature visualization by activation maximization."
We sought to bring in tools and techniques developed for studying ConvNets to
the study of biological neural networks. However, one key difference that
impedes direct translation of tools is that gradients can be obtained from
ConvNets using backpropagation, but such gradients are not available from the
brain. To circumvent this problem, we developed a method for gradient-free
activation maximization by combining a generative neural network with a genetic
algorithm. We termed this method XDream (EXtending DeepDream with real-time
evolution for activation maximization), and we have shown that this method can
reliably create strong stimuli for neurons in the macaque visual cortex (Ponce
et al., 2019). In this paper, we describe extensive experiments characterizing
the XDream method by using ConvNet units as in silico models of neurons. We
show that XDream is applicable across network layers, architectures, and
training sets; examine design choices in the algorithm; and provide practical
guides for choosing hyperparameters in the algorithm. XDream is an efficient
algorithm for uncovering neuronal tuning preferences in black-box networks
using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table
Deep Learning with Photonic Neural Cellular Automata
Rapid advancements in deep learning over the past decade have fueled an
insatiable demand for efficient and scalable hardware. Photonics offers a
promising solution by leveraging the unique properties of light. However,
conventional neural network architectures, which typically require dense
programmable connections, pose several practical challenges for photonic
realizations. To overcome these limitations, we propose and experimentally
demonstrate Photonic Neural Cellular Automata (PNCA) for photonic deep learning
with sparse connectivity. PNCA harnesses the speed and interconnectivity of
photonics, as well as the self-organizing nature of cellular automata through
local interactions to achieve robust, reliable, and efficient processing. We
utilize linear light interference and parametric nonlinear optics for
all-optical computations in a time-multiplexed photonic network to
experimentally perform self-organized image classification. We demonstrate
binary classification of images in the fashion-MNIST dataset using as few as 3
programmable photonic parameters, achieving an experimental accuracy of 98.0%
with the ability to also recognize out-of-distribution data. The proposed PNCA
approach can be adapted to a wide range of existing photonic hardware and
provides a compelling alternative to conventional photonic neural networks by
maximizing the advantages of light-based computing whilst mitigating their
practical challenges. Our results showcase the potential of PNCA in advancing
photonic deep learning and highlights a path for next-generation photonic
computers
Understanding How Image Quality Affects Deep Neural Networks
Image quality is an important practical challenge that is often overlooked in
the design of machine vision systems. Commonly, machine vision systems are
trained and tested on high quality image datasets, yet in practical
applications the input images can not be assumed to be of high quality.
Recently, deep neural networks have obtained state-of-the-art performance on
many machine vision tasks. In this paper we provide an evaluation of 4
state-of-the-art deep neural network models for image classification under
quality distortions. We consider five types of quality distortions: blur,
noise, contrast, JPEG, and JPEG2000 compression. We show that the existing
networks are susceptible to these quality distortions, particularly to blur and
noise. These results enable future work in developing deep neural networks that
are more invariant to quality distortions.Comment: Final version will appear in IEEE Xplore in the Proceedings of the
Conference on the Quality of Multimedia Experience (QoMEX), June 6-8, 201
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
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