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Found 14 papers, 4 papers with code
Deep Learning for Tumor Classification in Imaging Mass Spectrometry
Deep learning offers an approach to learn feature extraction and classification combined in a single model.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts.
A Closer Look at Double Backpropagation
In recent years, an increasing number of neural network models have included derivatives with respect to inputs in their loss functions, resulting in so-called double backpropagation for first-order optimization.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing.
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging.
Structure preserving deep learning
Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network.
Equivariant neural networks for inverse problems
In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach.
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution.
CAFLOW: Conditional Autoregressive Flows
We model the conditional distribution of the latent encodings by modeling the auto-regressive distributions with an efficient multi-scale normalizing flow, where each conditioning factor affects image synthesis at its respective resolution scale.
Invertible Learned Primal-Dual
We propose invertible Learned Primal-Dual as a method for tomographic image reconstruction.
Conditional Image Generation with Score-Based Diffusion Models
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling.
Non-Uniform Diffusion Models
We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows.
