On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine learning system's ability to generalize beyond the biases of a training set. OOD benchmarks are designed to present a different joint distribution of data and labels between training and test time. VQA-CP has become the standard OOD benchmark for visual question answering, but we discovered three troubling practices in its current use. First, most published methods rely on explicit knowledge of the construction of the OOD splits. They often rely on ``inverting'' the distribution of labels, e.g. answering mostly 'yes' when the common training answer is 'no'. Second, the OOD test set is used for model selection. Third, a model's in-domain performance is assessed after retraining it on in-domain splits (VQA v2) that exhibit a more balanced distribution of labels. These three practices defeat the objective of evaluating generalization, and put into question the value of methods specifically designed for this dataset. We show that embarrassingly-simple methods, including one that generates answers at random, surpass the state of the art on some question types. We provide short- and long-term solutions to avoid these pitfalls and realize the benefits of OOD evaluation

JPEG-like Image Compression using Neural-network-based Block Classification and Adaptive Reordering of Transform Coefficients

The research described in this thesis addresses aspects of coding of discrete-cosinetransform (DCT) coefficients, that are present in a variety of transform-based digital-image-compression schemes such as JPEG. Coefficient reordering; that directly affects the symbol statistics for entropy coding, and therefore the effectiveness of entropy coding; is investigated. Adaptive zigzag reordering, a novel versatile technique that achieves efficient reordering by processing variable-size rectangular sub-blocks of coefficients, is developed. Classification of blocks of DCT coefficients using an artificial neural network (ANN) prior to adaptive zigzag reordering is also considered. Some established digital-image-compression techniques are reviewed, and the JPEG standard for the DCT-based method is studied in more detail. An introduction to artificial neural networks is provided. Lossless conversion of blocks of coefficients using adaptive zigzag reordering is investigated, and experimental results are presented. A versatile algorithm, that generates zigzag scan paths for sub-blocks of any dimensions using a binary decision tree, is developed. An implementation of the algorithm based on programmable logic devices (PLDs) is described demonstrating the feasibility of hardware implementations. Coding of the sub-block dimensions, that need to be retained in order to reconstruct a sub-block during decoding, based on the scan-path length is developed. Lossy conversion of blocks of coefficients is also considered, and experimental results are presented. A two-layer feedforward artificial neural network trained using an error-backpropagation algorithm, that determines the sub-block dimensions, is described. Isolated nonzero coefficients of small significance are discarded in some blocks, and therefore smaller sub-blocks are generated

Navigation control of an automated mobile robot robot using neural network technique

Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue is the provision of sensors to study the earth. As neural networks (NNs) are described by adaptability and a fitness for managing non-linear problems, they are conceived to be useful when utilized on navigation robots. In this exploration our computerized reasoning framework is focused around neural network model for control of an Automated motion robot in eccentric and unsystematic nature. Hence the back propagation algorithm has been utilized for controlling the direction of the mobile robot when it experiences by an obstacle in the left, right and front directions. The recreation of the robot under different deterrent conditions is carried out utilizing Arduino which utilizes C programs for usage

MACHINE LEARNING - TECHNIQUES

This article provides a comprehensive overview of software development expertise using machine learning techniques (MLT). Machine learning in this new era demonstrates the commitment to consistently make accurate estimates. The machine learning system effectively “learns” how to evaluate from the training package of completed projects. The main goal and contribution of the review is to support research on expert assessment, i.e. to facilitate other researchers to make relevant expert assessment studies using machine learning techniques. This article presents commonly used machine learning techniques such as neural networks for expert evaluation in the field of software development, case-based reasoning, classification and regression trees, induction, genetic algorithm and genetic programming. In each of our studies, we found that the results of different machine learning techniques depend on the areas in which they are used. The review of our study not only indicates that these techniques compete with traditional evaluators in a data set, but also illustrate that these methods are sensitive to the data on which they are trained

Status and recommendations of technological and data-driven innovations in cancer care:Focus group study

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations