66,835 research outputs found
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive task. As it has to compute million of
parameters, it results to huge memory consumption. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity.
With the introduction of Fully Convolutional Neural Network, which uses finer
strides and utilizes deconvolutional layers for upsampling, it has been a go to
for any image segmentation task. In this paper, we propose two segmentation
architecture which not only needs one-third the parameters to compute but also
gives better accuracy than the similar architectures. The model weights were
transferred from the popular neural net like VGG19 and VGG16 which were trained
on Imagenet classification data-set. Then we transform all the fully connected
layers to convolutional layers and use dilated convolution for decreasing the
parameters. Lastly, we add finer strides and attach four skip architectures
which are element-wise summed with the deconvolutional layers in steps. We
train and test on different sparse and fine data-sets like Pascal VOC2012,
Pascal-Context and NYUDv2 and show how better our model performs in this tasks.
On the other hand our model has a faster inference time and consumes less
memory for training and testing on NVIDIA Pascal GPUs, making it more efficient
and less memory consuming architecture for pixel-wise segmentation.Comment: 8 page
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
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