OCD: Learning to Overfit with Conditional Diffusion ModelsDownload PDF

Published: 01 Feb 2023, Last Modified: 22 Oct 2023Submitted to ICLR 2023Readers: Everyone
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.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning
TL;DR: Local learning with a hypernetwork that employs a diffusion process
Supplementary Material: zip
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.00471/code)
40 Replies