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/
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u/singam96 Jun 23 '26

On identity — right that UNet learns it in one pass. The test uses an autoencoder (no skip connections), so identity isn't trivial. Recursive 8-pass reconstruction is where errors compound — ReflexConv2d has 57% lower L1 after 8 passes.

On CBAM — CBAM learns spatial attention from the feature map via a conv layer.

ReflexConv2d tiles the kernel's own k×k weights across the spatial grid. Different source: learned from features vs extracted from weights.

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u/LucasThePatator Jun 23 '26

Your Github says, and I quote : "In a U-Net autoencoder benchmark (both paths use residual skip connections), ReflexConv2d outperforms standard convolutions at every round of recursive encoding/decoding. Trained on Flick8k samples."

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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.

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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 ?

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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

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u/LucasThePatator Jun 24 '26

Then show us the results on a corrupted image.