Semi-Supervised Deep Learning for Fully Convolutional Networks

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.Comment: 9 pages, 6 figure

Models of Visual Attention in Deep Residual CNNs

Feature reuse from earlier layers in neural network hierarchies has been shown to improve the quality of features at a later stage - a concept known as residual learning. In this thesis, we learn effective residual learning methodologies infused with attention mechanisms to observe their effect on different tasks. To this end, we propose 3 architectures across medical image segmentation and 3D point cloud analysis. In FocusNet, we propose an attention based dual branch encoder decoder structure that learns an extremely efficient attention mechanism which achieves state of the art results on the ISIC 2017 skin cancer segmentation dataset. We propose a novel loss enhancement that improves the convergence of FocusNet, performing better than state-of-the-art loss functions such as tversky and focal loss. Evaluations of the architecture proposes two drawbacks which we fix in FocusNetAlpha. Our novel residual group attention block based network forms the backbone of this architecture, learning distinct features with sparse correlations, which is the key reason for its effectiveness. At the time of writing this thesis, FocusNetAlpha outperforms all state-of-the-art convolutional autoencoders with the least parameters and FLOPs compared to them, based on our experiments on the ISIC 2018, DRIVE retinal vessel segmentation and the cell nuclei segmentation dataset. We then shift our attention to 3D point cloud processing where we propose SAWNet, which combines global and local point embeddings infused with attention, to create a spatially aware embedding that outperforms both. We propose a novel method to learn a global feature aggregation for point clouds via a fully differential block that does not need a lot of trainable parameters and gives obvious performance boosts. SAWNet beats state-of-the-art results on ModelNet40 and ShapeNet part segmentation datasets

Embedding Diversity: Communication and Label Concept for Underutilized Crops – Checklist for your First Evaluation

There are some very successful cases of marketing of underutilized crops in European countries. Examples are the marketing of rare varieties in Austrian and Swiss supermarkets. Other examples are market initiatives in Spain, France, or Italy, selling local produces and processed foods in specialised shops, at farmers markets, or directly at the farm. A consumer survey was conducted in 2017 in France, Italy, Spain, and Switzerland during DIVERSIFOOD project in order to gain additional data on such examples; and Rossi et al. (2016)1 developed the DIVERSIFOOD Case Study Framework. Among others an objective of these studies was to identify some best-case examples for the use of a trademark, label or logo and the related communication strategies. Then a flagship approach to communicate the benefits of underutilized crops by means of a label should be developed. The studies identified many individual approaches of the various marketing initiatives, each based on slightly different secrets of success. Within the DIVERSIFOOD consortium it was concluded that there’s no sense in defining a single best-label flagship approach; instead, a concept should give an idea of the possibilities to communicate the value of agrobiodiversity to consumers. This concept is the resulting outcome. It provides a structured approach for networks an market initiatives to evaluate whether a label for underutilized crops is feasible, and wheter it is in line with theire values and aims or not, and what the premises for the communication strategy are for those underutilized crops. This concept provides a general idea about topics that should be considered. The result could be the introduction of a label or the abandonment of introducing a label. In the beginning of such an evaluation there are most likely existing underutilized crops, that is, genetic resources, such as old, locally, and newly bred underutilized varieties, as well as some products or product ideas derived from these crops. The main target groups of this concept are farmers involved in participatory breeding, seed savers, seed networks and communities, foundations, and breeders of underutilized crops, as well as partners of such genetic resources. Moreover, the concept could be interesting for initiatives and organizations that consider the integration of underutilized crops in their existing label; however there is no specific treatment of this topic in the concept. The concept should be considered as the first step in the evaluation of a new label. The treated topics might also hint on further considerations when developing a successful marketing initiative. However, the concept focuses mainly on two points: 1. Introduction of a label2, a logo, a trademark or similar and some implications 2. Communication tools and communication contents for products of underutilized crop

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.Comment: ACL 201