The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the predictive performance of the model, or produce explanations that are not transparent, trustworthy or do not capture the diversity of the data. To address these shortcomings, we propose ProtoVAE, a variational autoencoder-based ...

Preservation of local similarity structure is a key challenge in deep clustering. Many recent deep clustering methods therefore use autoencoders to help guide the model's neural network towards an embedding which is more reflective of the input space geometry. However, recent work has shown that autoencoder-based deep clustering models can suffer from objective function mismatch (OFM). In order to ...

Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features ...

Standard strategies for fully supervised semantic segmentation of medical images require large pixel-level annotated datasets. This makes such methods challenging due to the manual labor required and limits the usability when segmentation is needed for new classes for which data is scarce. Few-shot segmentation (FSS) is a recent and promising direction within the deep learning literature designed ...

Forecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems. Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform conventional models that include AutoRegressive Integrated Moving Average(ARIMA). However, due to the structural limitation of vanilla RNN which holds unit-length internal ...

Data without annotation are easy to obtain in the real-world, however, established supervised learning methods are not applicable to analyze them. Several learning approaches have been proposed in recent years to exploit the underlying structure of the data without requiring annotations. Semi-supervised learning aims to improve the predictive performance of these unsupervised approaches, by exploiting ...

Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the comprehensibility and traceability of the underlying decision-making strategies. As a remedy, numerous post-hoc and self-explanation methods have been developed to interpret the models’ behavior. Those methods, in addition, ...

Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. Yet, as most try-on approaches fit in-shop garments onto a target person, they require the laborious and restrictive construction of a paired training dataset, severely limiting their scalability. While a few recent works attempt to transfer garments ...

Tumor segmentation is a crucial but difficult task in treatment planning and follow-up of cancerous patients. The challenge of automating the tumor segmentation has recently received a lot of attention, but the potential of utilizing hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI), a novel and promising imaging modality in oncology, is still under-explored. Recent ...