2 code implementations • 23 Sep 2022 • Chen Liu, Matthew Amodio, Liangbo L. Shen, Feng Gao, Arman Avesta, Sanjay Aneja, Jay C. Wang, Lucian V. Del Priore, Smita Krishnaswamy
To address this, we present CUTS (Contrastive and Unsupervised Training for multi-granular medical image Segmentation), a fully unsupervised deep learning framework for medical image segmentation to better utilize the vast majority of imaging data that are not labeled or annotated.
no code implementations • 23 Jun 2020 • Matthew Amodio, Rim Assouel, Victor Schmidt, Tristan Sylvain, Smita Krishnaswamy, Yoshua Bengio
Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.
no code implementations • 25 Sep 2019 • Matthew Amodio, Smita Krishnaswamy
Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution.
no code implementations • 25 Sep 2019 • Matthew Amodio, David van Dijk, Ruth Montgomery, Guy Wolf, Smita Krishnaswamy
While generative neural networks can learn to transform a specific input dataset into a specific target dataset, they require having just such a paired set of input/output datasets.
2 code implementations • CVPR 2019 • Matthew Amodio, Smita Krishnaswamy
The achievements of these models have been limited to a particular subset of domains where this assumption yields good results, namely homogeneous domains that are characterized by style or texture differences.
1 code implementation • 25 Jan 2019 • David van Dijk, Daniel Burkhardt, Matthew Amodio, Alex Tong, Guy Wolf, Smita Krishnaswamy
Here, we propose a reformulation of the problem such that the goal is to learn a non-linear transformation of the data into a latent archetypal space.
no code implementations • 24 Jan 2019 • Matthew Amodio, Smita Krishnaswamy
Unsupervised domain mapping has attracted substantial attention in recent years due to the success of models based on the cycle-consistency assumption.
1 code implementation • ICLR 2019 • Alexander Tong, David van Dijk, Jay S. Stanley III, Matthew Amodio, Kristina Yim, Rebecca Muhle, James Noonan, Guy Wolf, Smita Krishnaswamy
Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian penalty to structure the activations within a layer.
1 code implementation • ICML 2018 • Matthew Amodio, Smita Krishnaswamy
We present a new GAN called the Manifold-Aligning GAN (MAGAN) that aligns two manifolds such that related points in each measurement space are aligned together.
no code implementations • 25 May 2017 • Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
During generation, NAMs make significantly fewer violations of the constraints of the underlying grammar than RNNs trained only on samples from the language of the grammar.