We are concerned with the vulnerability of computer vision models to distributional shifts. We cast this problem in terms of combinatorial optimization, evaluating the regions in the input space where a (black-box) model is more vulnerable. This is carried out by combining image transformations from a given set and standard search algorithms. We embed this idea in a training procedure, where we define new data augmentation rules over iterations, accordingly to the image transformations that the current model is most vulnerable to. An empirical evaluation on classification and semantic segmentation problems suggests that the devised algorithm allows to train models more robust against content-preserving image transformations, and in general, against distributional shifts.

On a variety of high-stakes tasks, machine learning algorithms are on the threshold of doing what human experts do with such high fidelity that we are contemplating using their predictions as a substitute for human output. For example, convolutional neural networks are close to diagnosing pneumonia from chest X-rays better than radiologists can [14, 15]; examples like these underpin much of the widespread discussion of algorithmic automation in these tasks. In assessing the potential for algorithms, however, the community has implicitly equated the specific task of prediction with the general task of automation. We argue here that this implicit correspondence misses key aspects of the automation problem; a broader conceptualization of automation can lead directly to concrete benefits in some of the key application areas where this process is unfolding. We start from the premise that automation is more than just the replacement of human effort on a task; it is also the meta-decision of which instances of the task to automate. And it is here that algorithms distinguish themselves from earlier technology used for automation, because they can actively take part in this decision of what to automate. But as currently constructed, they are not set up to help with this second part of the problem. The automation problem, then, should involve an algorithm that on any given instance both (i) produces a prediction output; and (ii) additionally also produces a triage judgment of its effectiveness relative to the human effort it would replace on that instance.

The impending demise of Moore's Law has begun to broadly impact the computing research community.38 Moore's Law has driven the computing industry for many decades, with nearly every aspect of society benefiting from the advance of improved computing processors, sensors, and controllers. Behind these products has been a considerable research industry, with billions of dollars invested in fields ranging from computer science to electrical engineering. Fundamentally, however, the exponential growth in computing described by Moore's Law was driven by advances in materials science.30,37 From the start, the power of the computer has been limited by the density of transistors. Progressive advances in how to manipulate silicon through advancing lithography methods and new design tools have kept advancing computing in spite of perceived limitations of the dominant fabrication processes of the time.37 There is strong evidence that this time is indeed different, and Moore's Law is soon to be over for good.3,38 Already, Dennard scaling, Moore's Law's lesser known but equally important parallel, appears to have ended.11 Dennard's scaling refers to the property that the reduction of transistor size came with an equivalent reduction of required power.8

During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20 m between the bit and high-fidelity rock type sensors. The only way to detect the lithotype changes in time is the usage of Measurements While Drilling (MWD) data. However, there are no mathematical modeling approaches that reconstruct the rock type based on MWD data with high accuracy. In this article, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock type. We propose the approach that combines traditional machine learning based on the solution of the rock type classification problem with change detection procedures rarely used before in Oil & Gas industry. The data come from a newly developed oilfield in the North of Western Siberia. The results suggest that we can detect a significant part of changes in rock type reducing the change detection delay from 20 to 2.6 m and the number of false positive alarms from 71 to 7 per well.

Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. To solve this challenging problem, meta-learning has become a popular paradigm that advocates the ability to "learn to adapt". Recent works have shown, however, that simple learning strategies without meta-learning could be competitive. In this paper, we go a step further and show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform current meta-learning techniques. Our approach consists of designing an ensemble of deep networks to leverage the variance of the classifiers, and introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity. Evaluation is conducted on the mini-ImageNet and CUB datasets, where we show that even a single network obtained by distillation yields state-of-the-art results.

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-art results in various domains such as image recognition and natural language processing. One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available. To keep on improving the performance of DL, increasing the scalability of DL systems is necessary. In this survey, we perform a broad and thorough investigation on challenges, techniques and tools for scalable DL on distributed infrastructures. This incorporates infrastructures for DL, methods for parallel DL training, multi-tenant resource scheduling and the management of training and model data. Further, we analyze and compare 11 current open-source DL frameworks and tools and investigate which of the techniques are commonly implemented in practice. Finally, we highlight future research trends in DL systems that deserve further research.

Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the other hand, facial micro-expressions generally represent the actual emotion of a person, as it is a spontaneous reaction expressed through human face. Despite of a few attempts made for recognizing micro-expressions, still the problem is far from being a solved problem, which is depicted by the poor rate of accuracy shown by the state-of-the-art methods. A few CNN based approaches are found in the literature to recognize micro-facial expressions from still images. Whereas, a spontaneous micro-expression video contains multiple frames that have to be processed together to encode both spatial and temporal information. This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework. The MicroExpSTCNN considers the full spatial information, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion of the eyes and mouth regions. The experiments are performed over CAS(ME)^2 and SMIC micro-expression databases. The proposed MicroExpSTCNN model outperforms the state-of-the-art methods.

Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.

Atrial fibrillation is a cardiac arrhythmia that affects an estimated 33.5 million people globally and is the potential cause of 1 in 3 strokes in people over the age of 60. Detection and diagnosis of atrial fibrillation (AFIB) is done noninvasively in the clinical environment through the evaluation of electrocardiograms (ECGs). Early research into automated methods for the detection of AFIB in ECG signals focused on traditional bio-medical signal analysis to extract important features for use in statistical classification models. Artificial intelligence models have more recently been used that employ convolutional and/or recurrent network architectures. In this work, significant time and frequency domain characteristics of the ECG signal are extracted by applying the short-time Fourier trans-form and then visually representing the information in a spectrogram. Two different classification approaches were investigated that utilized deep features in the spectrograms construct-ed from ECG segments. The first approach used a pretrained DenseNet model to extract features that were then classified using Support Vector Machines, and the second approach used the spectrograms as direct input into a convolutional network. Both approaches were evaluated against the MIT-BIH AFIB dataset, where the convolutional network approach achieved a classification accuracy of 93.16%. While these results do not surpass established automated atrial fibrillation detection methods, they are promising and warrant further investigation given they did not require any noise prefiltering, hand-crafted features, nor a reliance on beat detection.

Capacity analysis has been introduced in [2] as a way to analyze which dependencies a linear model is focussing its modelling capacity on, when trained on a given task. The concept was then extended in [3] to neural networks with nonlinear activations, where capacity propagation through layers was studied. When the layers are residual (or differential), and in one limiting case with extremely irregular activations (which was called the pseudo-random limit), it has been shown that capacity propagation through layers follows a discrete Markov equation. This discrete equation can then be approximated by a continuous Kolmogorov forward equation in the deep limit, provided some specific scaling relation holds between the network depth and the scale of its residual connections - more precisely, the residual weights must scale as the inverse square root of the number of layers. Following [1], it was then hypothesized that the success of residual networks lies in their ability to propagate capacity through a large number of layers in a non-degenerate manner. It is interesting to note that the inverse square root scaling mentioned above is the only scaling relation that leads to a non-degenerate propagation PDE in that case: larger weights would lead to shattering, while smaller ones would lead to no spatial propagation at all. In this paper, we take this idea one step further and formulate the conjecture that enforcing the right scaling relations - i.e. the ones that lead to a non-degenerate continuous limit for capacity propagation - is key to avoiding the shattering problem: we call this the neural network scaling conjecture. In the example above, this would mean that the inverse square root scaling must be enforced if one wants to use residual networks at their full power. In the second part of this paper, we use the PDE capacity propagation framework to study a number of commonly used network architectures, and determine the scaling relations that are required for a non-degenerate capacity propagation to happen in each case.