Protect privacy of deep classification networks by exploiting their generative power

Chen, Jiyu; Guo, Yiwen; Zheng, Qianjun; Chen, Hao

doi:10.1007/s10994-021-05951-6

Cited by 10 publications

(4 citation statements)

References 21 publications

(40 reference statements)

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“…To balance the contributions of three losses, we intentionally tone down the weights of the direction and distribution losses. In the future, we will try some automatic methods [52], [53], [54] to further optimize their weights. Fig.…”

Section: B Loss Functionsmentioning

confidence: 99%

Doing More With Moiré Pattern Detection in Digital Photos

Yang

Yan³

et al. 2023

IEEE Trans. on Image Process.

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Detecting moiré patterns in digital photographs is meaningful as it provides priors towards image quality evaluation and demoiréing tasks. In this paper, we present a simple yet efficient framework to extract moiré edge maps from images with moiré patterns. The framework includes a strategy for training triplet (natural image, moiré layer, and their synthetic mixture) generation, and a Moiré Pattern Detection Neural Network (MoireDet) for moiré edge map estimation. This strategy ensures consistent pixel-level alignments during training, accommodating characteristics of a diverse set of camera-captured screen images and real-world moiré patterns from natural images. The design of three encoders in MoireDet exploits both high-level contextual and low-level structural features of various moiré patterns. Through comprehensive experiments, we demonstrate the advantages of MoireDet: better identification precision of moiré images on two datasets, and a marked improvement over state-of-the-art demoiréing methods.

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Section: B Loss Functionsmentioning

confidence: 99%

Doing More With Moiré Pattern Detection in Digital Photos

Yang

Yan³

et al. 2023

IEEE Trans. on Image Process.

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“…However, this approach may not be effective when there is significant variation in the difficulty of learning across tasks [222]. Thus, various weighting strategies have been proposed and developed, such as uncertainty weights [230], gradient normalization [231], the dynamic weight average [227], the projecting conflicting gradient [232], impartial multitask learning [233], and random loss weighting [229].…”

Section: Multitask Learningmentioning

confidence: 99%

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Chato,

Regentova

2023

JPM

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Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

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“…Adaptive weights are used to dynamically adjust the weights assigned to each loss term in the total loss function during training. This is implemented using the GradNorm method [56]. This is done in order to address the issue of gradient pathology, where loss terms with higher derivatives tend to dominate the total gradient vector and negatively affect the accuracy of the solution.…”

Section: Training Validation and Testing Datamentioning

confidence: 99%

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

Singh,

Ebongue,

Rezaei

et al. 2023

Batteries

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Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.

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Protect privacy of deep classification networks by exploiting their generative power

Cited by 10 publications

References 21 publications

Doing More With Moiré Pattern Detection in Digital Photos

Doing More With Moiré Pattern Detection in Digital Photos

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

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