11–20 of 23 posts
Can someone smarter than me explain what this is about?
Glossing through the paper, here's my take. Someone previously found that that the cross-attention layers in text-to-image diffusion models captures correlation between the input text tokens and corresponding image regions, so that one can use this to segment the image, pixels containing "cat" for example. However this segmentation was rather coarse. The authors of this paper found that also using the self-attention…
Makes you wonder what intelligence is lurking in a 10T parameter model like Gemini 3 that we may not discover for some years yet…
This is a cool result. Deep learning image models are trained on enormous amounts of data and the information recorded in their weights continues to astonish me. Over in the Stable Diffusion space, hobbyists (as opposed to professional researchers) are continuing to find new ways to squeeze intelligence out of models that were trained in 2022 and are considerably out of date compared with the latest “flow matching” m…
A large part of my 2017 phd thesis [0] is dedicated in exploring the formulation and utility of soft validation operators, including this soft IoU, and the extent to which they are "better" / "more reliable" than thresholding (whether this occurs in isolation, or even when marginalised out, as in with the AUC). Long story short, soft operators are at least an order of magnitude more reliable than their thresholding counterparts [1], despite the fact that thresholding still seems to be the industry/academia standard. This is the case for any set-operation-based operator, such as the Dice coefficient (a.k.a. F1-score), not just for the IoU. Recently, influential groups have proposed the matthews correlation coefficient as a "better operator", but still treat it in binary / thresholding terms, which means it's still unreliable to an order of magnitude. I suspect this insight goes beyond images (e.g. the F1-score is often used in ML problems more generally, in situations where probabilistic outputs are thresholded to compare against binary ground truth labels), but I haven't tested that hypothesis explicitly beyond the image domain (yet).
In this work you effectively used the "goedel" (i.e. min/max) fuzzy operator to define fuzzy intersection and union, for the purposes of using it in an IoU operator. There are other fuzzy norms with interesting properties that you can also explore. Other classical ones include product and lukasiewicz. I show in [0] and [1] that these have "best case scenario sub-pixel overlap", "average case" and "worst-case scenario" underlying semantics. (In other words, min/max should not be a random choice of T-norm, but a conscious choice which should match your problem, and what the operator is intended to validate specifically). In my own work, I then proceeded show that if you take gradient direction at the boundary into account, you can come up with a fuzzy intersection/union pair which has directional semantics, and is even more reliable an operator when used to define a soft IoU.
Having said that, in your case you're comparing against a binary ground truth. This collapses all the different T-norms to the same value. I wonder if this is the reason you chose a binary ground truth. If yes, you might want to consider my work, and use original 'soft' ground truths instead, for higher reliability, as well as ability to define intersection semantics.
I hope the above is of interest / use to you :) (and, if you were to decide to cite my work, it wouldn't be the eeeeeend of the world, I gueeeeesss xD )
[0] https://ora.ox.ac.uk/objects/uuid:dc352697-c804-4257-8aec-08...
[1] https://repository.essex.ac.uk/24856/1/Papastylianou.etal201...
This is a cool result. Deep learning image models are trained on enormous amounts of data and the information recorded in their weights continues to astonish me. Over in the Stable Diffusion space, hobbyists (as opposed to professional researchers) are continuing to find new ways to squeeze intelligence out of models that were trained in 2022 and are considerably out of date compared with the latest “flow matching” m…
Hey, do you know how you figured out about this information? I would be super curious to keep track of current ad-hoc ways of pushing older models to do cooler things. LMK
This is a cool result. Deep learning image models are trained on enormous amounts of data and the information recorded in their weights continues to astonish me. Over in the Stable Diffusion space, hobbyists (as opposed to professional researchers) are continuing to find new ways to squeeze intelligence out of models that were trained in 2022 and are considerably out of date compared with the latest “flow matching” m…
I've got a new potential LoRA implementation that I've been testing locally (using a transformed S matrix with frozen U and V weights from an SVD decomposition of the base matrix) that seems to work really well, and I've been playing with both changes to the forward-noising schedule and the loss functions which seem to yield empirically superior results of the standard way of doing things. Epsilon prediction may be old and busted (and working on it makes me really appreciate flow matching!) but there's some really cool stuff happening in its training dynamics that are a lot of fun to explore.
It's just a lot of fun. Great playground for both learning how these things work and for trying out new ideas.
This is a cool result. Deep learning image models are trained on enormous amounts of data and the information recorded in their weights continues to astonish me. Over in the Stable Diffusion space, hobbyists (as opposed to professional researchers) are continuing to find new ways to squeeze intelligence out of models that were trained in 2022 and are considerably out of date compared with the latest “flow matching” m…
This is a cool result. Deep learning image models are trained on enormous amounts of data and the information recorded in their weights continues to astonish me. Over in the Stable Diffusion space, hobbyists (as opposed to professional researchers) are continuing to find new ways to squeeze intelligence out of models that were trained in 2022 and are considerably out of date compared with the latest “flow matching” m…
Stable Diffusion 1.5 is a great model for hacking on. It's powerful enough that it encodes some really rich semantics, but small and light enough that iterative hacking on it is quick enough that it can be done by hobbyists. I've got a new potential LoRA implementation that I've been testing locally (using a transformed S matrix with frozen U and V weights from an SVD decomposition of the base matrix) that seems to w…
Earlier quoted context omitted.
Stable Diffusion 1.5 is a great model for hacking on. It's powerful enough that it encodes some really rich semantics, but small and light enough that iterative hacking on it is quick enough that it can be done by hobbyists. I've got a new potential LoRA implementation that I've been testing locally (using a transformed S matrix with frozen U and V weights from an SVD decomposition of the base matrix) that seems to w…
I’d love to follow your work. Got a GitHub?
https://gist.github.com/cheald/7d9a436b3f23f27b8d543d805b77f... - here's a quick dump of my SVDLora module though. I wrote it for use in OneTrainer though it should be adaptable to other frameworks easily enough. If you want to try it out, I'd love to hear what you find.