OCD: Learning to Overfit with Conditional Diffusion Models
- URL: http://arxiv.org/abs/2210.00471v5
- Date: Fri, 9 Jun 2023 04:55:02 GMT
- Title: OCD: Learning to Overfit with Conditional Diffusion Models
- Authors: Shahar Lutati and Lior Wolf
- Abstract summary: We present a dynamic model in which the weights are conditioned on an input sample x.
We learn to match those weights that would be obtained by finetuning a base model on x and its label y.
- Score: 95.1828574518325
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 approximated by a denoising diffusion model. The diffusion
model we employ focuses on modifying a single layer of the base model and is
conditioned on the input, activations, and output of this layer. Since the
diffusion model is stochastic in nature, multiple initializations generate
different networks, forming an ensemble, which leads to further improvements.
Our experiments demonstrate the wide applicability of the method for image
classification, 3D reconstruction, tabular data, speech separation, and natural
language processing. Our code is available at
https://github.com/ShaharLutatiPersonal/OCD
Related papers
- Non-Normal Diffusion Models [3.5534933448684134]
Diffusion models generate samples by incrementally reversing a process that turns data into noise.
We show that when the step size goes to zero, the reversed process is invariant to the distribution of these increments.
We demonstrate the effectiveness of these models on density estimation and generative modeling tasks on standard image datasets.
arXiv Detail & Related papers (2024-12-10T21:31:12Z) - Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding [84.3224556294803]
Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences.
We aim to optimize downstream reward functions while preserving the naturalness of these design spaces.
Our algorithm integrates soft value functions, which looks ahead to how intermediate noisy states lead to high rewards in the future.
arXiv Detail & Related papers (2024-08-15T16:47:59Z) - Simplified and Generalized Masked Diffusion for Discrete Data [47.711583631408715]
Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data.
In this work, we aim to provide a simple and general framework that unlocks the full potential of masked diffusion models.
arXiv Detail & Related papers (2024-06-06T17:59:10Z) - Glauber Generative Model: Discrete Diffusion Models via Binary Classification [21.816933208895843]
We introduce the Glauber Generative Model (GGM), a new class of discrete diffusion models.
GGM deploys a Markov chain to denoise a sequence of noisy tokens to a sample from a joint distribution of discrete tokens.
We show that it outperforms existing discrete diffusion models in language generation and image generation.
arXiv Detail & Related papers (2024-05-27T10:42:13Z) - Denoising Diffusion Bridge Models [54.87947768074036]
Diffusion models are powerful generative models that map noise to data using processes.
For many applications such as image editing, the model input comes from a distribution that is not random noise.
In our work, we propose Denoising Diffusion Bridge Models (DDBMs)
arXiv Detail & Related papers (2023-09-29T03:24:24Z) - Reflected Diffusion Models [93.26107023470979]
We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data.
Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
arXiv Detail & Related papers (2023-04-10T17:54:38Z) - SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image.
It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales.
Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
arXiv Detail & Related papers (2022-11-22T18:00:03Z) - Unifying Diffusion Models' Latent Space, with Applications to
CycleDiffusion and Guidance [95.12230117950232]
We show that a common latent space emerges from two diffusion models trained independently on related domains.
Applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors.
arXiv Detail & Related papers (2022-10-11T15:53:52Z) - Understanding Diffusion Models: A Unified Perspective [0.0]
Diffusion models have shown incredible capabilities as generative models.
We review, demystify, and unify the understanding of diffusion models across both variational and score-based perspectives.
arXiv Detail & Related papers (2022-08-25T09:55:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.