Antonio Rodriguez-Sanchez

David Peer, Sebastian Stabinger, Stefan Engl et al.

Fine-tuning transformer models after unsupervised pre-training reaches a very high performance on many different natural language processing tasks. Unfortunately, transformers suffer from long inference times which greatly increases costs in production. One possible solution is to use knowledge distillation, which solves this problem by transferring information from large teacher models to smaller student models. Knowledge distillation maintains high performance and reaches high compression rates, nevertheless, the size of the student model is fixed after pre-training and can not be changed individually for a given downstream task and use-case to reach a desired performance/speedup ratio. Another solution to reduce the size of models in a much more fine-grained and computationally cheaper fashion is to prune layers after the pre-training. The price to pay is that the performance of layer-wise pruning algorithms is not on par with state-of-the-art knowledge distillation methods. In this paper, Greedy-layer pruning is introduced to (1) outperform current state-of-the-art for layer-wise pruning, (2) close the performance gap when compared to knowledge distillation, while (3) providing a method to adapt the model size dynamically to reach a desired performance/speedup tradeoff without the need of additional pre-training phases. Our source code is available on https://github.com/deepopinion/greedy-layer-pruning.

Josef Gugglberger, David Peer, Antonio Rodriguez-Sanchez

Capsule networks are a type of neural network that have recently gained increased popularity. They consist of groups of neurons, called capsules, which encode properties of objects or object parts. The connections between capsules encrypt part-whole relationships between objects through routing algorithms which route the output of capsules from lower level layers to upper level layers. Capsule networks can reach state-of-the-art results on many challenging computer vision tasks, such as MNIST, Fashion-MNIST, and Small-NORB. However, most capsule network implementations use two to three capsule layers, which limits their applicability as expressivity grows exponentially with depth. One approach to overcome such limitations would be to train deeper network architectures, as it has been done for convolutional neural networks with much increased success. In this paper, we propose a methodology to train deeper capsule networks using residual connections, which is evaluated on four datasets and three different routing algorithms. Our experimental results show that in fact, performance increases when training deeper capsule networks. The source code is available on https://github.com/moejoe95/res-capsnet.

David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez

Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this paper, we introduce a novel methodology to identify layers that decrease the test accuracy of trained models. Conflicting layers are detected as early as the beginning of training. In the worst-case scenario, we prove that such a layer could lead to a network that cannot be trained at all. A theoretical analysis is provided on what is the origin of those layers that result in a lower overall network performance, which is complemented by our extensive empirical evaluation. More precisely, we identified those layers that worsen the performance because they would produce what we name conflicting training bundles. We will show that around 60% of the layers of trained residual networks can be completely removed from the architecture with no significant increase in the test-error. We will further present a novel neural-architecture-search (NAS) algorithm that identifies conflicting layers at the beginning of the training. Architectures found by our auto-tuning algorithm achieve competitive accuracy values when compared against more complex state-of-the-art architectures, while drastically reducing memory consumption and inference time for different computer vision tasks. The source code is available on https://github.com/peerdavid/conflicting-bundles

David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez

Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this paper, we introduce a novel theory and metric to identify layers that decrease the test accuracy of the trained models, this identification is done as early as at the beginning of training. In the worst-case, such a layer could lead to a network that can not be trained at all. More precisely, we identified those layers that worsen the performance because they produce conflicting training bundles as we show in our novel theoretical analysis, complemented by our extensive empirical studies. Based on these findings, a novel algorithm is introduced to remove performance decreasing layers automatically. Architectures found by this algorithm achieve a competitive accuracy when compared against the state-of-the-art architectures. While keeping such high accuracy, our approach drastically reduces memory consumption and inference time for different computer vision tasks.

David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez

A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the output of capsules from lower-level layers to upper-level layers. In this paper, we prove that state-of-the-art routing procedures decrease the expressivity of capsule networks. More precisely, it is shown that EM-routing and routing-by-agreement prevent capsule networks from distinguishing inputs and their negative counterpart. Therefore, only symmetric functions can be expressed by capsule networks, and it can be concluded that they are not universal approximators. We also theoretically motivate and empirically show that this limitation affects the training of deep capsule networks negatively. Therefore, we present an incremental improvement for state-of-the-art routing algorithms that solves the aforementioned limitation and stabilizes the training of capsule networks.

David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez

In this paper we introduce a new inductive bias for capsule networks and call networks that use this prior $γ$-capsule networks. Our inductive bias that is inspired by TE neurons of the inferior temporal cortex increases the adversarial robustness and the explainability of capsule networks. A theoretical framework with formal definitions of $γ$-capsule networks and metrics for evaluation are also provided. Under our framework we show that common capsule networks do not necessarily make use of this inductive bias. For this reason we introduce a novel routing algorithm and use a different training algorithm to be able to implement $γ$-capsule networks. We then show experimentally that $γ$-capsule networks are indeed more transparent and more robust against adversarial attacks than regular capsule networks.

Sebastian Stabinger, Antonio Rodriguez-Sanchez

Over the last couple of years, deep learning and especially convolutional neural networks have become one of the work horses of computer vision. One limiting factor for the applicability of supervised deep learning to more areas is the need for large, manually labeled datasets. In this paper we propose an easy to implement method we call guided labeling, which automatically determines which samples from an unlabeled dataset should be labeled. We show that using this procedure, the amount of samples that need to be labeled is reduced considerably in comparison to labeling images arbitrarily.

Sebastian Stabinger, Antonio Rodriguez-Sanchez

Convolutional Neural Networks have become state of the art methods for image classification over the last couple of years. By now they perform better than human subjects on many of the image classification datasets. Most of these datasets are based on the notion of concrete classes (i.e. images are classified by the type of object in the image). In this paper we present a novel image classification dataset, using abstract classes, which should be easy to solve for humans, but variations of it are challenging for CNNs. The classification performance of popular CNN architectures is evaluated on this dataset and variations of the dataset that might be interesting for further research are identified.

Sebastian Stabinger, Antonio Rodriguez-Sanchez, Justus Piater

Humans are generally good at learning abstract concepts about objects and scenes (e.g.\ spatial orientation, relative sizes, etc.). Over the last years convolutional neural networks have achieved almost human performance in recognizing concrete classes (i.e.\ specific object categories). This paper tests the performance of a current CNN (GoogLeNet) on the task of differentiating between abstract classes which are trivially differentiable for humans. We trained and tested the CNN on the two abstract classes of horizontal and vertical orientation and determined how well the network is able to transfer the learned classes to other, previously unseen objects.