Keuper Labs


Archival Workshop Paper (in CVPRw Proceedings)

An Extended Study of Human-Like Behavior Under Adversarial Training

Paul Gavrikov, Janis Keuper, Margret Keuper

Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by small perturbations to inputs that are often imperceivable to humans and do not have to carry semantic meaning. Adversarial training poses a partial solution to address this issue by training models on worst-case perturbations. Yet, recent work has also pointed out that the reasoning in neural networks is different from humans. Humans identify objects by shape, while neural nets mainly employ texture cues. Exemplarily, a model trained on photographs will likely fail to generalize to datasets containing sketches. Interestingly, it was also shown that adversarial training seems to favorably increase the shift toward shape bias. In this work, we revisit this observation and provide an extensive analysis of this effect on various architectures, the common L_2- and L_-training, and Transformer-based models. Further, we provide a possible explanation for this phenomenon from a frequency perspective.


Happy People – Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models

Steffen Jung, Jan Christian Schwedhelm, Claudia Schillings, Margret Keuper

In recent years, optimization in the learned latent space of deep generative models has been successfully applied to black-box optimization problems such as drug design, image generation or neural architecture search. Existing models thereby leverage the ability of neural models to learn the data distribution from a limited amount of samples such that new samples from the distribution can be drawn. In this work, we propose a novel image generative approach that optimizes the generated sample with respect to a continuously quantifiable property. While we anticipate absolutely no practically meaningful application for the proposed framework, it is theoretically principled and allows to quickly propose samples at the mere boundary of the training data distribution. Specifically, we propose to use tree-based ensemble models as mathematical programs over the discrete latent space of vector quantized VAEs, which can be globally solved. Subsequent weighted retraining on these queries allows to induce a distribution shift. In lack of a practically relevant problem, we consider a visually appealing application: the generation of happily smiling faces (where the training distribution only contains less happy people) - and show the principled behavior of our approach in terms of improved FID and higher smile degree over baseline approaches.


Non-Archival Workshop Paper

Fine-Grained Product Classification on Leaflet Advertisements

Daniel Ladwig, Bianca Lamm, Janis Keuper

In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, by Text, as well as by Image and Text. The approach “Classification by Text” uses the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%. We release our code at cc.

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Neural Architecture Design and Robustness: A Dataset

Steffen Jung1, Jovita Lukasik, Margret Keuper

Finding architectures that are (more) robust against perturbations requires expensive evaluations. We introduce a database on neural architecture design and robustness evaluations to facilitate research in this direction. For this, we evaluate a whole neural architecture search space (NASBench-201) on a range of common adversarial attacks and corruption types. We further present three exemplary use cases of this dataset, in which we (i) benchmark robustness measurements based on Jacobian and Hessian matrices for their robustness predictability, (ii) perform neural architecture search on robust accuracies, and (iii) provide an initial analysis of how architectural design choices affect robustness. We find that carefully crafting the topology of a network can have substantial impact on its robustness, where networks with the same parameter count range in mean adversarial robust accuracy from 20% − 41%. Code and data is available at

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Intra-Source Style Augmentation for Improved Domain Generalization

Yumeng Li, Dan Zhang, Margret Keuper, Anna Khoreva

The generalization with respect to domain shifts, as they frequently appear in applications such as autonomous driving, is one of the remaining big challenges for deep learning models. Therefore, we propose an intra-source style augmentation (ISSA) method to improve domain generalization in semantic segmentation. Our method is based on a novel masked noise encoder for StyleGAN2 inversion. The model learns to faithfully reconstruct the image, preserving its semantic layout through noise prediction. Random masking of the estimated noise enables the style mixing capability of our model, i.e. it allows to alter the global appearance without affecting the semantic layout of an image. Using the proposed masked noise encoder to randomize style and content combinations in the training set, ISSA effectively increases the diversity of training data and reduces spurious correlation. As a result, we achieve up to 11.3% mIoU improvements on driving-scene semantic segmentation under domain shifts, e.g., adverse weather conditions.


Assessing Domain Generalization of Semantic Segmenters with Synthetic Data

Yumeng Li, Dan Zhang, Margret Keuper, Anna Khoreva