Impact Of Semantics, Physics And Adversarial Mechanisms In Deep Learning

Deep learning has greatly advanced the performance of algorithms on tasks such as image classification, speech enhancement, sound separation, and generative image models. However many current popular systems are driven by empirical rules that do not fully exploit the underlying physics of the data. Many speech and audio systems fix STFT preprocessing before their networks. Hyperspectral Image (HSI) methods often don't deliberately consider the spectral spatial trade off that is not present in normal images. Generative Adversarial Networks (GANs) that learn a generative distribution of images don't prioritize semantic labels of the training data. To meet these opportunities we propose to alter known deep learning methods to be more dependent on the semantic and physical underpinnings of the data to create better performing and more robust algorithms for sound separation and classification, image generation, and HSI segmentation. Our approaches take inspiration from from Harmonic Analysis, SVMs, and classical statistical detection theory, and further the state-of-the art in source separation, defense against audio adversarial attacks, HSI classification, and GANs. Recent deep learning approaches have achieved impressive performance on speech enhancement and separation tasks. However, these approaches have not been investigated for separating mixtures of arbitrary sounds of different types, a task we refer to as universal sound separation. To study this question, we develop a dataset of mixtures containing arbitrary sounds, and use it to investigate the space of mask-based separation architectures, varying both the overall network architecture and the framewise analysis-synthesis basis for signal transformations. We compare using a short-time Fourier transform (STFT) with a learnable basis at variable window sizes for the feature extraction stage of our sound separation network. We also compare the robustness to adversarial examples of speech classification networks that similarly hybridize established Time-frequency (TF) methods with learnable filter weights. We analyze HSI images for material classification. For hyperspectral image cubes TF methods decompose spectra into multi-spectral bands, while Neural Networks (NNs) incorporate spatial information across scales and model multiple levels of dependencies between spectral features. The Fourier scattering transform is an amalgamation of time-frequency representations with neural network architectures. We propose and test a three dimensional Fourier scattering method on hyperspectral datasets, and present results that indicate that the Fourier scattering transform is highly effective at representing spectral data when compared with other state-of-the-art methods. We study the spectral-spatial trade-off that our Scattering approach allows.We also use a similar multi-scale approach to develop a defense against audio adversarial attacks. We propose a unification of a computational model of speech processing in the brain with commercial wake-word networks to create a cortical network, and show that it can increase resistance to adversarial noise without a degradation in performance. Generative Adversarial Networks are an attractive approach to constructing generative models that mimic a target distribution, and typically use conditional information (cGANs) such as class labels to guide the training of the discriminator and the generator. We propose a loss that ensures generator updates are always class specific, rather than training a function that measures the information theoretic distance between the generative distribution and one target distribution, we generalize the successful hinge-loss that has become an essential ingredient of many GANs to the multi-class setting and use it to train a single generator classifier pair. While the canonical hinge loss made generator updates according to a class agnostic margin a real/fake discriminator learned, our multi-class hinge-loss GAN updates the generator according to many classification margins. With this modification, we are able to accelerate training and achieve state of the art Inception and FID scores on Imagenet128. We study the trade-off between class fidelity and overall diversity of generated images, and show modifications of our method can prioritize either each during training. We show that there is a limit to how closely classification and discrimination can be combined while maintaining sample diversity with some theoretical results on K+1 GANs

Pre-training method in the tasks of obtaining surrogate models of gas turbine units for gas turbine electric power stations

This article focuses on the application of pre-training methods in the task of synthesizing surrogate models. The article emphasizes that pre-training significantly improves the accuracy of surrogate models and speeds up their creation process. The authors examine pre-training’s impact on various aspects of surrogate modeling of a gas turbine unit that is part of a gas turbine electric power station, such as reducing computational costs, improving the approximation of complex processes, and optimizing the model synthesis procedure. The work demonstrates specific examples that clearly show how the use of pre-training can significantly improve the performance of surrogate models and optimize the development process. Thus, the authors convincingly argue that pre-training is a key tool for increasing the efficiency of surrogate modeling, capable of significantly reducing the time, costs, and efforts required for the development and use of surrogate models in the energy sector

Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations

The article is devoted to the current task of selecting pre-training parameters for the synthesis of surrogate models, which is a key factor in creating high-performance models of complex technological objects. During the study, the authors conduct a systematic analysis of various parameters and their interactions, including determining the optimal number of training iterations, the number of trainable layers, and the number of neurons in these layers. Thanks to this approach, the results of the presented study can significantly improve the accuracy and efficiency of surrogate models, which in turn leads to simplification and acceleration of the process of their development and application in various fields of science and engineering

Pre-training method in the tasks of obtaining surrogate models of gas turbine units for gas turbine electric power stations

This article focuses on the application of pre-training methods in the task of synthesizing surrogate models. The article emphasizes that pre-training significantly improves the accuracy of surrogate models and speeds up their creation process. The authors examine pre-training’s impact on various aspects of surrogate modeling of a gas turbine unit that is part of a gas turbine electric power station, such as reducing computational costs, improving the approximation of complex processes, and optimizing the model synthesis procedure. The work demonstrates specific examples that clearly show how the use of pre-training can significantly improve the performance of surrogate models and optimize the development process. Thus, the authors convincingly argue that pre-training is a key tool for increasing the efficiency of surrogate modeling, capable of significantly reducing the time, costs, and efforts required for the development and use of surrogate models in the energy sector

Selection of pre-training parameters for synthesizing surrogate models of gas turbine units for gas turbine electro power stations

The article is devoted to the current task of selecting pre-training parameters for the synthesis of surrogate models, which is a key factor in creating high-performance models of complex technological objects. During the study, the authors conduct a systematic analysis of various parameters and their interactions, including determining the optimal number of training iterations, the number of trainable layers, and the number of neurons in these layers. Thanks to this approach, the results of the presented study can significantly improve the accuracy and efficiency of surrogate models, which in turn leads to simplification and acceleration of the process of their development and application in various fields of science and engineering