Credit: ‘ Striving for Disentanglement’ by Simon Greig — https://www.flickr.com/photos/xrrr/500039281/

Background: Disentanglement in Representation learning

On Thursday evening of the conference week, as I sauntered…



Class-wise mean images of the 10 handwritten digits in the Kannada MNIST dataset

TLDR:

I am disseminating 2 datasets:
Kannada-MNIST dataset: 28X 28 grayscale images: 60k Train | 10k Test
Dig-MNIST: 28X 28 grayscale images: 10240 (1024x10) {See pic below}


TLDR: They seeded their webscrape via REDDIT, the mother lode of all ideas tinderboxy and weaponizable. So, it will at the very least be a PR disaster if they release the bigger model. The smaller 117M is nasty as is sans the subtleties!

Table of Contents

Intro:

One paper that I recommend my interns to read is Unbiased Look at Dataset Bias that has these awesome introductory lines:

Torralba, Antonio, and Alexei A. Efros. “Unbiased look at dataset bias.” (2011): 1521–1528.


A rant on collective amnesia and faux-ancientization of atrocities


Prelude:

Let’s begin with a simple introduction into the world of adversarial inputs. These are inputs into a machine learning classifier that have been shrewdly perturbed in such a way that these changes are near damn invisible to the naked eye but can fool the machine learning classifier into predicting either…

Vinay Prabhu

PhD, Carnegie Mellon University. Chief Scientist at UnifyID Inc

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