In this section I collect the posts of those papers I read and I wrote a tiny summary about. I sometimes write literal sentences from the papers if I think it’s important and cannot be summarized better. Papers are sorted by year.
. Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger. 2017. On Calibration of Modern Neural Networks.
. Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang. 2017. Large-batch training for Deep Learning: Generalization gap and Minima.
. Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe. 2014. Qualitatively Characterizing Neural Network Optimization Problems.
. Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio. 2013. Maxout Networks.
. Matthew A. Turk and Alex P. Pentland. 1991. Face recognition using Eigenfaces.
Dear sir my area of interest is image denoising/image super resolution using convolutional neural network with python (Tensorflow).
sir plz i confused about coding how can code it, so sir plz suggest me a video link or image super resolution.
Best Regards
Hi niaz,
Both image denoising and image super resolution are none of the fields I usually work on, and my only recommendation is a general one: read papers and play around with Tensorflow. It takes time and be patient. If you are motivated, you won’t give up! On the other hand, I have no idea about any video on super resolution.
Regards,
JM.