OCD: Learning to Overfit with Conditional Diffusion Models

Shahar Lutati, Lior Wolf

Keywords: Local learning, hypernetworks, diffusion processes

Abstract: We present a dynamic model in which the weights are conditioned on an input sample $x$ and are learned to match those that would be obtained by finetuning a base model on $x$ and its label $y$. This mapping between an input sample and network weights is shown to be approximated by a linear transformation of the sample distribution, which suggests that a denoising diffusion model can be suitable for this task. The diffusion model we therefore employ focuses on modifying a single layer of the base model and is conditioned on the input, activations, and output of this layer. Our experiments demonstrate the wide applicability of the method for image classification, 3D reconstruction, tabular data, and speech separation. Our code is attached as supplementary.

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TL;DR: Local learning with a hypernetwork that employs a diffusion process

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/ocd-learning-to-overfit-with-conditional/code)