Shobhita Sundaram
@shobsund.bsky.social
PhD student at MIT working on deep learning (representation learning, generative models, synthetic data, alignment). ssundaram21.github.io
Personal vision tasks–like detecting *your mug*--are hard; they’re data scarce and fine-grained. In our new paper, we show you can adapt general-purpose vision models to these tasks from just three photos! 📝: arxiv.org/abs/2412.16156 💻: github.com/ssundaram21/... (1/n)
I'm at #NeurIPS2024 this week! Excited to present our work on perceptual alignment with Stephanie Fu on Thursday morning (poster #1302). Please reach out if you want to chat about synthetic data, alignment, data-centric AI, representation learning, or anything else!
Phillip Isola@phillipisola.bsky.social · 2y agoFirst up, "When Does Perceptual Alignment Benefit Vision Representations?" (arxiv.org/abs/2410.10817) This paper is about distance between embeddings. It says: measure how humans perceive distance, then adjust a neural net to match. This improves transfer to lots of tasks (but not all tasks).
