Semantic bottleneck for computer vision tasks

This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point in which the representation of the image is entirely expressed with natural language , while retaining the efficiency of numerical representations. We show that our approach is able to generate semantic representations that give state-of-the-art results on semantic content-based image retrieval and also perform very well on image classification tasks. Intelligibility is evaluated through user centered experiments for failure detection

Contrastive examples for addressing the tyranny of the majority

Computer vision algorithms, e.g. for face recognition, favour groups of individuals that are better represented in the training data. This happens because of the generalization that classifiers have to make. It is simpler to fit the majority groups as this fit is more important to overall error. We propose to create a balanced training dataset, consisting of the original dataset plus new data points in which the group memberships are intervened, minorities become majorities and vice versa. We show that current generative adversarial networks are a powerful tool for learning these data points, called contrastive examples. We experiment with the equalized odds bias measure on tabular data as well as image data (CelebA and Diversity in Faces datasets). Contrastive examples allow us to expose correlations between group membership and other seemingly neutral features. Whenever a causal graph is available, we can put those contrastive examples in the perspective of counterfactuals

Visual Rationalizations in Deep Reinforcement Learning for Atari Games

Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games. However, clearly explaining why a certain action is taken by the agent can be as important as the decision itself. Deep reinforcement learning models, as other deep learning models, tend to be opaque in their decision-making process. In this work, we propose to make deep reinforcement learning more transparent by visualizing the evidence on which the agent bases its decision. In this work, we emphasize the importance of producing a justification for an observed action, which could be applied to a black-box decision agent.Comment: presented as oral talk at BNAIC 201

Inverse Classification for Comparison-based Interpretability in Machine Learning

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint. This principle is implemented using observation generation in the Growing Spheres algorithm. Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.Comment: preprin

Arctic Sea Ice Volume and Mass from Data Fusion of CryoSat-2 and SMOS

The quantification of the sea ice mass balance as the marine part of the cryosphere by satellite observations depend on sea ice thickness data records for the entire ice-covered oceans. The challenges to this task are numerous. Sea ice itself is a highly dynamic medium with a significant variability at meter scale and a strong seasonal cycle which significantly impacts it remote sensing signature. Satellite sensors must therefore provide precise observations at high spatial resolution to observe the full spread of the sea ice thickness distribution and its governing processes such as the dynamic deformation. Average thickness values for larger areas are sufficient for mass balance estimates, however, available methods such as satellite altimetry and passive microwave remote sensing rely on indirect methods and auxiliary information and are often not able to provide information with an acceptable uncertainty for certain or thickness categories or during the presence of surface melt. In addition, suitable satellite sensors in orbits that enabling sea ice thickness retrieval in the inner Arctic Ocean have been in service only until recently in comparison to satellites capable of observing sea ice area. Thus, the assessment of the sea ice mass balance for longer time series is often based on reanalysis models and not Earth Observation data. The sea ice community also traditionally expresses the total sea ice budget volume and not mass. We will therefore present an available sea ice volume data record that is derived by data fusion of CryoSat-2 radar altimeter and SMOS L-Band passive microwave-based sea ice thickness information. Both methods have a complementary sensitivity to different thickness classes and optimal interpolation is employed for gap-less sea ice thickness information in the northern hemisphere since November 2010. The data record is generated for the ESA funded MOS & CryoSat-2 Sea Ice Data Product Processing and Dissemination Service (CS2SMOS-PDS). We discuss the characteristics of the data set and provide an overview of intended evolutions of the data set, specifically improvements to the spatial resolutions, a potential extension to the southern hemisphere and the addition of other available satellite sensors to the optimal interpolation. Within the context of the mass balance of the cryosphere we will share our thoughts on the significance of the CryoSat-2/SMOS based sea ice volume time series for climate applications in the context of its comparable short temporal and how this information can be presented more consistently to other components of the cryosphere

Explainable AI: The new 42?

Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge. There has been recent and relatively rapid success of AI/machine learning solutions arises from neural network architectures. A new generation of neural methods now scale to exploit the practical applicability of statistical and algebraic learning approaches in arbitrarily high dimensional spaces. But despite their huge successes, largely in problems which can be cast as classification problems, their effectiveness is still limited by their un-debuggability, and their inability to “explain” their decisions in a human understandable and reconstructable way. So while AlphaGo or DeepStack can crush the best humans at Go or Poker, neither program has any internal model of its task; its representations defy interpretation by humans, there is no mechanism to explain their actions and behaviour, and furthermore, there is no obvious instructional value.. the high performance systems can not help humans improve. Even when we understand the underlying mathematical scaffolding of current machine learning architectures, it is often impossible to get insight into the internal working of the models; we need explicit modeling and reasoning tools to explain how and why a result was achieved. We also know that a significant challenge for future AI is contextual adaptation, i.e., systems that incrementally help to construct explanatory models for solving real-world problems. Here it would be beneficial not to exclude human expertise, but to augment human intelligence with artificial intelligence

Prescribing practices of primary-care veterinary practitioners in dogs diagnosed with bacterial pyoderma

Concern has been raised regarding the potential contributions of veterinary antimicrobial use to increasing levels of resistance in bacteria critically important to human health. Canine pyoderma is a frequent, often recurrent diagnosis in pet dogs, usually attributable to secondary bacterial infection of the skin. Lesions can range in severity based on the location, total area and depth of tissue affected and antimicrobial therapy is recommended for resolution. This study aimed to describe patient signalment, disease characteristics and treatment prescribed in a large number of UK, primary-care canine pyoderma cases and to estimate pyoderma prevalence in the UK vet-visiting canine population