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View Code? Open in Web Editor NEWAuto-Encoding Transformations (AETv1), CVPR 2019
Home Page: https://arxiv.org/abs/1901.04596
Auto-Encoding Transformations (AETv1), CVPR 2019
Home Page: https://arxiv.org/abs/1901.04596
res = np.dot(np.linalg.inv(A.T * A) * A.T, B)
What is the purpose of the above statement? It is giving an error:
"numpy.linalg.linalg.LinAlgError: Singular matrix"
I tried using replacing np.linalg.inv
with numpy.linalg.pinv
. But still it is giving ValueError of shape inconsistency.
Thanks.
Hi,
Thanks for sharing the code with us. I am curious why do we need to pre-generate and save the projective transformation parameters. This doesn't seem like an expensive computation, which should be OK to compute on the fly. Specifically:
Generate and save 0.5 million projective transformation parameters:
python save_homography.py
Thank you!
res = numpy.dot(numpy.linalg.inv(A.T * A) * A.T, B)
What do A and B denote?
And how to understand the operation above?
Your paper is excellent, but I find the snippet of code is obscure.
Hello, Guo-Jun. Thanks for your great work of AET and making your code available.
Can you also provide the file of pre-trained models on ImageNet for the convenience of experiments?
Thank you again for your valuable attention.
Hi Liheng,
General affine transformation or projective transformation can bring black areas at image border. Do these artifacts help network learn trivial shortcuts, such as predicting transformation parameters just according to black area's shape. Your paper did not mention this issue, and I wonder is there some special process to solve this.
Hope to get your reply. Thanks!
Hi, Liheng.
I have reproduced supervised NIN as the paper setting, which is 88.23 in my experiment results, but the result of the paper report is 92.80. Will 92.8 be the result of running your code or refer to the results in other people's papers?
Thank you.
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