chirag126 / vog Goto Github PK

Thank you for sharing the amazing work!

When I run the toy example, it runs perfectly fine and shows the exact same result you uploaded in this repo.
Below are the command and the result.

• command

  $ python3 toy_script.py --split test --mode early --vog_cal normalize
  

• result
  

However, I found that the code uses the gradient of the softmax layer w.r.t. the input, which differs from the paper in that the pre-softmax layer is used in the paper. So I changed a single line of toy_script.py as below and got a somewhat weird result when I run the code again.

• change
  
• result
  

What did I miss here?

While reading paper, I get confused about the conflicts between figure and the contents.

Section 3.5, VoG understands early and late training dynamics part

• In the early training stage, samples having higher VoG scores have a lower average error rate as the gradient updates hinge on easy examples.
• This phenomenon reverses during the late-stage of the training, where, across all datasets, high VoG scores in the late-stage have the highest error rates as updates to the challenging examples dominate the computation of variance.

What I understand is below

• In early-stage training, easy examples have high VoG score
• In late-stage training, difficult examples have high VoG score

But Figure 2 seems different what I expected.

Early-stage training with highest VoG score doesn't look like easy examples, however lowest VoG score seems quite easy examples.

Is there something I'm missing?

Hi,
While running this code, because the gradients w.r.t all images is of the dimension [50000,32,32,3], it seems that my CUDA is running out of memory very soon .

Hi there, I came across your work when I went to the last CVPR and have been thinking for quite some time on how to apply this for image super-resolution. Currently, the script is for classification networks with a labelled training this, how can this be adapted for tasks which do not have predicted scores or labels?
Thanks