Blog

Learn About Our Meetup

5000+ Members

MEETUPS

LEARN, CONNECT, SHARE

Join our meetup, learn, connect, share, and get to know your Toronto AI community. 

JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[Discussion] Can anyone explain the pixelwise accuracy metric used in this paper? Also a question to the KL Divergence Loss.

So I am making a project based on this paper:

https://arxiv.org/ftp/arxiv/papers/1901/1901.07761.pdf

In this paper, a U-Net is used to generate optimized mechanical structures. I am trying to recreate the model and use it on my own generated data.

Now I have two questions:

  1. In 7.1 a pixelwise accuracy is mentioned. Right now I am using the default Keras metric “accuracy”, which isn’t reaching even close to the accuracy in the paper. (it starts at 0.3ish and goes to like 0.45). What I always do is to manually compare the generated structures to the ground truth in the training set. There are often models which have better accuracy, but the structures make less sense. What accuracy metric did they use in the paper?
  2. In the paper under 4.2.1, the KL Divergence is mentioned. My problem was, that the KL Divergence turned negative after an epoch or two (an Indicator that I don’t work with probability distributions?), so I switched to binary cross-entropy, which provides good results, but it is still bothering me , that I cant use the proposed loss method. Another point is the L2 Regularization: I get the best results using 1e-7 or lower as the l2 value, which is really low compared to the normally used values. What does that indicate?

Another point I wanted to mention: the dimensions of my data is a little bit different from the ones used in the paper: I use 65×49 as my Input Dimension.

I would really appreciate if someone can help me in fixing the problems.

submitted by /u/avdalim
[link] [comments]

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.