Kedar Tatwawadi

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We address the problem of adding colors to gray scale images. We use convolutional neural-nets because color of a pixel is strongly dependent on the features of its neighbors. In this work, we use a pre-trained neural net (VGG) as a base-network to extract features. We then add a featurecompression layer and small conv-net on top of it to perform the task(More)
MOTIVATION The dramatic decrease in the cost of sequencing has resulted in the generation of huge amounts of genomic data, as evidenced by projects such as the UK10K and the Million Veteran Project, with the number of sequenced genomes ranging in the order of 10 K to 1 M. Due to the large redundancies among genomic sequences of individuals from the same(More)
There has been a tremendous surge in the amount of data generated. New types of data, such as Genomic data [1], 3D-360 degree VR Data, Autonomous Driving Point Cloud data are being generated. A lot of human effort is spent in analyzing the statistics of these new data formats for designing good compressors. We know from Information theory that good(More)
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