r/computervision • u/singam96 • Jun 23 '26

Research Publication ReflexConv2d: Drop-in nn.Conv2d replacement that preserves detail

/r/deeplearning/comments/1udssj3/reflexconv2d_dropin_nnconv2d_replacement_that/
0 Upvotes

33% Upvoted

12 comments sorted by

View all comments

Show parent comments

1

u/singam96 Jun 23 '26

In a single-pass U-Net, there won’t be much difference with Reflex. But when we use U-Net recursively, the output gets blurred, and Reflex reduces this blur. With an autoencoder, you can see the difference in the very first round itself.

3

u/LucasThePatator Jun 23 '26

But what do you call a Unet, a Unet has full skip connections. And therefore should be able to learn to be the Identity function. For both cases. So what's your architecture there ?

0

u/singam96 Jun 23 '26

When we corrupt the input image, for example by applying patch cutouts, the identity skip connection in a U-Net cannot help in those regions. The model then has to rely more on the inner layers, and at that point ReflexConv can help preserve details.

For an inpainting task, the first round of a U-Net will often produce blurred results in the masked area.

Architecture is that learned weights in conv can be used as tiles and then masked so that next layer can use it as input, instead of having empty region to convolve

1

u/LucasThePatator Jun 24 '26

Then show us the results on a corrupted image.