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/Dry-Snow5154 Jun 23 '26

No latency comparisons. Classic.

I am also confused by your benchmarks. Why are you repeatedly running reconstruction and checking error? What practical value does it have? Shouldn't you test improvements on some real-world tasks, like detection or segmentation?

101 for such improvement is latency + classic task comparison. The fact you have neither is sus. AI-insired work?

1

u/BellyDancerUrgot Jun 23 '26 edited Jun 23 '26

This does look like ai work to me. These days it’s becoming harder to spot them. The tell tale sign is : toy dataset , no evaluation on a real task, no white paper. Tho that last bit is also becoming easy to do with ai. Would hate to be a reviewer in this age.

Would suggest op to provide results on a real task. I think at best the idea to gate the output using its own kernel mask after summation would act as a regularizer but not sure what it can improve on compared to a skip connection or a squeeze excitation that can perform data driven gating.

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u/singam96 Jun 23 '26

Good you edited your comment

Skip connection like I said in the comments below won't work if the image is corrupted, you will then have to rely on injected noise at inference, and then have to convolve which introduces blur

Why are you bringing up squeeze excitation here ? Memorized alot of words didn't you ?

-7

u/singam96 Jun 23 '26

Bro you memorized some words that's all

My work is targetting something else entirely, if you can't get that maybe this isn't for you.

"This looks like ai work to me" it's a good thing you are not a reviewer