3 research outputs found
Auto-Rotating Perceptrons
This paper proposes an improved design of the perceptron unit to mitigate the
vanishing gradient problem. This nuisance appears when training deep multilayer
perceptron networks with bounded activation functions. The new neuron design,
named auto-rotating perceptron (ARP), has a mechanism to ensure that the node
always operates in the dynamic region of the activation function, by avoiding
saturation of the perceptron. The proposed method does not change the inference
structure learned at each neuron. We test the effect of using ARP units in some
network architectures which use the sigmoid activation function. The results
support our hypothesis that neural networks with ARP units can achieve better
learning performance than equivalent models with classic perceptrons.Comment: LatinX in AI Research Workshop at NeurIPS 201
Megapixel Photon-Counting Color Imaging using Quanta Image Sensor
Quanta Image Sensor (QIS) is a single-photon detector designed for extremely
low light imaging conditions. Majority of the existing QIS prototypes are
monochrome based on single-photon avalanche diodes (SPAD). Passive color
imaging has not been demonstrated with single-photon detectors due to the
intrinsic difficulty of shrinking the pixel size and increasing the spatial
resolution while maintaining acceptable intra-pixel cross-talk. In this paper,
we present image reconstruction of the first color QIS with a resolution of
pixels, supporting both single-bit and multi-bit photon
counting capability. Our color image reconstruction is enabled by a customized
joint demosaicing-denoising algorithm, leveraging truncated Poisson statistics
and variance stabilizing transforms. Experimental results of the new sensor and
algorithm demonstrate superior color imaging performance for very low-light
conditions with a mean exposure of as low as a few photons per pixel in both
real and simulated images
A Bit Too Much? High Speed Imaging from Sparse Photon Counts
Recent advances in photographic sensing technologies have made it possible to
achieve light detection in terms of a single photon. Photon counting sensors
are being increasingly used in many diverse applications. We address the
problem of jointly recovering spatial and temporal scene radiance from very few
photon counts. Our ConvNet-based scheme effectively combines spatial and
temporal information present in measurements to reduce noise. We demonstrate
that using our method one can acquire videos at a high frame rate and still
achieve good quality signal-to-noise ratio. Experiments show that the proposed
scheme performs quite well in different challenging scenarios while the
existing approaches are unable to handle them