Smart Augmentation - Learning an Optimal Data Augmentation Strategy

A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method which we call Smart Augmentation and we show how to use it to increase the accuracy and reduce overfitting on a target network. Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart Augmentation has shown the potential to increase accuracy by demonstrably significant measures on all datasets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets

Tax and Environmental Incentives to use Hydrocarbon-Based Air Conditioning Units in Thailand

Environmental protection is one of the most important global issues in the 21st Century, and ozone layer depletion is among the most serious environmental problems. The ozone layer is being destroyed by a group of manufactured chemicals containing chlorine and/or bromine. These chemicals are called ozone-depleting substances (ODS). The main ODSs are chlorofluorocarbons (CFCs) and hydrochlorofluorcarbons (HCFCs), which are mostly being used in refrigeration and air conditioning (RAC) systems. The Montreal Protocol is an international agreement to protect the ozone layer, and Thailand is one of the countries which have signed it. Under the Montreal Protocol, Thailand has committed herself to phase out and phase down the use of ODSs, but this would be impossible without a clear environmental tax law to control import/export and the use of ODSs and ODS-based refrigeration and air conditioning (AC) units. Thailand is listed as a developing country in the Article 5 countries of the Montreal Protocol, which states that all of the Article 5 countries should phase down and ban the use of ODSs with the help of laws and regulations. The situation in developed countries is totally different. They must ban the use of ODSs faster than Article 5 countries because due to their facilities doing these kinds of projects, is much more easily practicable and faster than in developing countries.There is no environmental tax law in Thailand to control or ban anufacturers from producing HCFC- and HFC-based AC units and the traders from importing them. This lack of an environmental tax law is a barrier to the incentivizing of the use of ODS alternatives, such as hydrocarbon (HC) based air conditioning units. In addition, the customs tariffs for all gasses used in AC units are the same, and all of them are under the same section in the Harmonized System (HS) code, so this is another disincentive to the use of HC-based AC units. This article looks at the environmental tax laws of Australia and the United States as the best means and most successful instances of control and banning of the use of ODSs and ODS-based equipment. This study suggests that Thailand must ratify an environmental tax law and amend the customs laws and regulations in order to achieve its obligations under the Montreal Protocol.Ultimately, the findings of this study aim to help Thailand develop a sustainable business model for HC-based air conditioning units among ASEAN countries, as the country plays a major role in the business of all of these countries

Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture

Deep neural networks are applied to a wide range of problems in recent years. In this work, Convolutional Neural Network (CNN) is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different networks are designed to perform depth estimation, each of them suitable for a feature level. Networks with different pooling sizes determine different feature levels. After designing a set of networks, these models may be combined into a single network topology using graph optimization techniques. This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common network layers, and can be further optimized by retraining to achieve an improved model compared to the individual topologies. In this study, four SPDNN models are trained and have been evaluated at 2 stages on the KITTI dataset. The ground truth images in the first part of the experiment are provided by the benchmark, and for the second part, the ground truth images are the depth map results from applying a state-of-the-art stereo matching method. The results of this evaluation demonstrate that using post-processing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data alongside the original data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth estimation. The computational time is also discussed in this study.Comment: 44 pages, 25 figure