CvxNet: Can an autoencoder reconstruct an image exactly?

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从第 4 页关于 CvxNet 自动编码器的讨论和 Figure 3 开始，然后使用列出的参数比较单图像和多图像情况下所报告的行为。完成标准是确定是否演示了精确重建，并阐明为什么矩形形状在多个训练图像中恢复效果不佳。

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Hi!
Thank you for your work!

In the paper I see that you show examples where 2D b/w images are used as the autoencoder input (Figure 3, page 4), so as the output you have object exactly reconstructed by the half-planes (lines in 2D space).

Tell me, please, whether such experiments were carried out? Does the network actually restore the shape exactly?

I am interested in this for the reason that I decided to conduct similar experiments for my own purposes (with 2d b/w images with oriented rectangles along the coordinate axes, one rectangle per image), but I can't train the network. For one image, the network is trained well (and work on this example well), but if I train it using multiple images, the predictions (even on the training sample) in most cases are very bad at restoring the shape of rectangles.
It seems like it should work.

Some parameters I use:
n_half_planes = 15 # with a margin, although the rectangle has only 4 sides
n_parts = 1 # one rectangle per image
dims = 2 # b/w images
lr = 1e-4
latent_size = 10
Also, I use more light backbone.

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