Search Results for author: Joshua Batson

Found 5 papers, 4 papers with code

Topological Obstructions to Autoencoding

no code implementations16 Feb 2021 Joshua Batson, C. Grace Haaf, Yonatan Kahn, Daniel A. Roberts

Using a series of illustrative low-dimensional examples, we show explicitly how the intrinsic and extrinsic topology of the dataset affects the behavior of an autoencoder and how this topology is manifested in the latent space representation during training.

Anomaly Detection Inductive Bias

Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​

1 code implementation NeurIPS 2020 Shreyas Fadnavis, Joshua Batson, Eleftherios Garyfallidis

Diffusion-weighted magnetic resonance imaging (DWI) is the only non-invasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain.

Denoising Self-Supervised Learning

Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning

1 code implementation2 Nov 2020 Shreyas Fadnavis, Joshua Batson, Eleftherios Garyfallidis

Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain.

Denoising Medical Diagnosis +1

Image Deconvolution via Noise-Tolerant Self-Supervised Inversion

1 code implementation11 Jun 2020 Hirofumi Kobayashi, Ahmet Can Solak, Joshua Batson, Loic A. Royer

We propose a general framework for solving inverse problems in the presence of noise that requires no signal prior, no noise estimate, and no clean training data.

Denoising Image Deconvolution

Noise2Self: Blind Denoising by Self-Supervision

3 code implementations30 Jan 2019 Joshua Batson, Loic Royer

We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data.

Denoising

Cannot find the paper you are looking for? You can Submit a new open access paper.