1 citations · 1 across the 13 of their papers we have counts for
14 papers
GIC-DLC: Differentiable Logic Circuits for Hardware-Friendly Grayscale Image Compression
Till Aczel, David F. Jenny, Simon Bührer +3
Neural image codecs achieve higher compression ratios than traditional hand-crafted methods such as PNG or JPEG-XL, but often incur substantial computational overhead, limiting the…
Efficient Bayesian Inference from Noisy Pairwise Comparisons
Till Aczel, Lucas Theis, Roger Wattenhofer
Evaluating generative models is challenging because standard metrics often fail to reflect human preferences. Human evaluations are more reliable but costly and noisy, as participa…
Virtual Fashion Photo-Shoots: Building a Large-Scale Garment-Lookbook Dataset
Yannick Hauri, Luca A. Lanzendörfer, Till Aczel
Fashion image generation has so far focused on narrow tasks such as virtual try-on, where garments appear in clean studio environments. In contrast, editorial fashion presents garm…
From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networks
Sven Brändle, Till Aczel, Andreas Plesner +1
Differentiable Logic Gate Networks (DLGNs) are a very fast and energy-efficient alternative to conventional feed-forward networks. With learnable combinations of logical gates, DLG…
The Impact of Scaling Training Data on Adversarial Robustness
Marco Zimmerli, Andreas Plesner, Till Aczel +1
Deep neural networks remain vulnerable to adversarial examples despite advances in architectures and training paradigms. We investigate how training data characteristics affect adv…
Light Differentiable Logic Gate Networks
Lukas Rüttgers, Till Aczel, Andreas Plesner +1
Differentiable logic gate networks (DLGNs) exhibit extraordinary efficiency at inference while sustaining competitive accuracy. But vanishing gradients, discretization errors, and…
The Unwinnable Arms Race of AI Image Detection
Till Aczel, Lorenzo Vettor, Andreas Plesner +1
The rapid progress of image generative AI has blurred the boundary between synthetic and real images, fueling an arms race between generators and discriminators. This paper investi…
HyperCool: Reducing Encoding Cost in Overfitted Codecs with Hypernetworks
Pep Borrell-Tatché, Till Aczel, Théo Ladune +1
Overfitted image codecs like Cool-chic achieve strong compression by tailoring lightweight models to individual images, but their encoding is slow and computationally expensive. To…
Recurrent Deep Differentiable Logic Gate Networks
Simon Bührer, Andreas Plesner, Till Aczel +1
While differentiable logic gates have shown promise in feedforward networks, their application to sequential modeling remains unexplored. This paper presents the first implementati…
Keep It Real: Challenges in Attacking Compression-Based Adversarial Purification
Samuel Räber, Till Aczel, Andreas Plesner +1
Previous work has suggested that preprocessing images through lossy compression can defend against adversarial perturbations, but comprehensive attack evaluations have been lacking…
Human Aligned Compression for Robust Models
Samuel Räber, Andreas Plesner, Till Aczel +1
Adversarial attacks on image models threaten system robustness by introducing imperceptible perturbations that cause incorrect predictions. We investigate human-aligned learned los…
Conditional Hallucinations for Image Compression
Till Aczel, Roger Wattenhofer
In lossy image compression, models face the challenge of either hallucinating details or generating out-of-distribution samples due to the information bottleneck. This implies that…
SUPClust: Active Learning at the Boundaries
Yuta Ono, Till Aczel, Benjamin Estermann +1
Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel acti…
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
Paul Doucet, Benjamin Estermann, Till Aczel +1
This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning, particularly within the context of self-supervised pre-trained…