Search Results for author: Ricard Durall

Found 16 papers, 5 papers with code

Adversarial Examples are Misaligned in Diffusion Model Manifolds

no code implementations12 Jan 2024 Peter Lorenz, Ricard Durall, Janis Keuper

In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results.

Adversarial Robustness Image Inpainting

Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality

no code implementations5 Jul 2023 Peter Lorenz, Ricard Durall, Janis Keuper

Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images.

DeepFake Detection

Deep Diffusion Models for Seismic Processing

no code implementations21 Jul 2022 Ricard Durall, Ammar Ghanim, Mario Fernandez, Norman Ettrich, Janis Keuper

Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing.

Decision Making Denoising

Asset Allocation: From Markowitz to Deep Reinforcement Learning

1 code implementation14 Jul 2022 Ricard Durall

Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon.

reinforcement-learning Reinforcement Learning (RL)

Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal

no code implementations24 Jun 2022 Ricard Durall, Ammar Ghanim, Norman Ettrich, Janis Keuper

To the best of our knowledge, this study pioneers the unboxing of neural networks for the demultiple process, helping the user to gain insights into the inside running of the network.

Investigating Shifts in GAN Output-Distributions

no code implementations28 Dec 2021 Ricard Durall, Janis Keuper

In this work, we introduce a loop-training scheme for the systematic investigation of observable shifts between the distributions of real training data and GAN generated data.

FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces

1 code implementation18 Oct 2021 Ricard Durall, Jireh Jam, Dominik Strassel, Moi Hoon Yap, Janis Keuper

We then incorporate the geometry information of a segmentation mask to provide a fine-grained manipulation of facial attributes.

Attribute Face Verification +3

Combining Transformer Generators with Convolutional Discriminators

no code implementations21 May 2021 Ricard Durall, Stanislav Frolov, Jörn Hees, Federico Raue, Franz-Josef Pfreundt, Andreas Dengel, Janis Keupe

Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks.

Data Augmentation Image Generation +1

Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues

no code implementations17 Dec 2020 Ricard Durall, Avraam Chatzimichailidis, Peter Labus, Janis Keuper

This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them.

Image Generation

Latent Space Conditioning on Generative Adversarial Networks

no code implementations16 Dec 2020 Ricard Durall, Kalun Ho, Franz-Josef Pfreundt, Janis Keuper

In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model.

Image Generation Representation Learning

Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions

2 code implementations CVPR 2020 Ricard Durall, Margret Keuper, Janis Keuper

Generative convolutional deep neural networks, e. g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences.

Local Facial Attribute Transfer through Inpainting

no code implementations7 Feb 2020 Ricard Durall, Franz-Josef Pfreundt, Janis Keuper

The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes.

Attribute Generative Adversarial Network

Unmasking DeepFakes with simple Features

5 code implementations2 Nov 2019 Ricard Durall, Margret Keuper, Franz-Josef Pfreundt, Janis Keuper

In this work, we present a simple way to detect such fake face images - so-called DeepFakes.

DeepFake Detection

Semi Few-Shot Attribute Translation

no code implementations8 Oct 2019 Ricard Durall, Franz-Josef Pfreundt, Janis Keuper

Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications.

Attribute Few-Shot Learning +3

Object Segmentation using Pixel-wise Adversarial Loss

no code implementations23 Sep 2019 Ricard Durall, Franz-Josef Pfreundt, Ullrich Köthe, Janis Keuper

Recent deep learning based approaches have shown remarkable success on object segmentation tasks.

Object Segmentation +1

Stabilizing GANs with Soft Octave Convolutions

1 code implementation29 May 2019 Ricard Durall, Franz-Josef Pfreundt, Janis Keuper

The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training.

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