Seems cool, but the image classification model benchmark choice is kinda weak given all the fun tools we have now. I wonder how Tversky probes do on top of DINOv3 for building a classifier for some task.
It's the same amount of effort benchmarking, just a better choice of backbone that enables better choices of benchmark tasks. If the claim is that a Tversky projection layer beats a linear projection layer today, then one can test whether that's true with foundation embedding models today.
It's also a more natural question to ask, since building projections on top of frozen foundation model embeddings is both common in an absolute sense, and much more common, relatively, than building projections off of tiny frozen networks like a ResNet-50.
throwawaymaths|6 months ago
no sense spending large amounts of compute on algorithms for new math unless you can prove it can crawl.
heyitsguay|6 months ago
It's also a more natural question to ask, since building projections on top of frozen foundation model embeddings is both common in an absolute sense, and much more common, relatively, than building projections off of tiny frozen networks like a ResNet-50.