On the occasion of the 1000th citation of our Sinkhorn-Knopp self-supervised representation learning paper, I've written a whole post about the history and the key bits of this method that powers the state-of-the-art SSL vision models.

Read it here :): docs.google.com/document/d/1...

Today we had a joint workshop between our FunAI Lab, UTN and AIST Japan. 13 talks, 1 cake and lots of Bavarian food really get research discussions going!
Towards more collaborations in AI between 🇩🇪 & 🇯🇵.
@hirokatukataoka.bsky.social

This means we can simply send an adapted RGB image to the server to get a personalised output.
We also show that the gains don't just come from adding a new learnable model, but instead from the interplay between the pretrained one and the PGN.

This CNN (e.g. running on a phone) outputs a softmax over a set of learned tokens. These are then combined and used for the adaptation. This allows efficient learning, but also for moving the signal back into pixel-space via pseudo-inverse.

Also known as reprogramming, works from @phillipisola.bsky.social showed that even adjusting singular pixels allows adapting a model. We take this one step further and make the input-only adaptation signal dependent on the image itself: We introduce a lightweight CNN, the Prompt Generation Network.