Rice Leaf Diseases Prediction using Deep Neural Networks with Transfer Learning

Abstract Transfer learning from natural images is well used in deep neural networks (DNNs) for medical image classification to achieve computer-aided clinical diagnosis. Although the adversarial vulnerability of DNNs hinders practical applications owing to the high stakes of diagnosis, adversarial attacks are expected to be limited because training data — which are often required for adversarial attacks — are generally unavailable in terms of security and privacy preservation. Nevertheless, we hypothesized that adversarial attacks are also possible using natural images because pre-trained models do not change significantly after fine-tuning. We focused on three representative DNN-based medical image classification tasks (i.e., skin cancer, referable diabetic retinopathy, and pneumonia classifications) and investigated whether medical DNN models with transfer learning are vulnerable to universal adversarial perturbations (UAPs), generated using natural images. UAPs from natural images are useful for both non-targeted and targeted attacks. The performance of UAPs from natural images was significantly higher than that of random controls, although slightly lower than that of UAPs from training images. Vulnerability to UAPs from natural images was observed between different natural image datasets and between different model architectures. The use of transfer learning causes a security hole, which decreases the reliability and safety of computer-based disease diagnosis. Model training from random initialization (without transfer learning) reduced the performance of UAPs from natural images; however, it did not completely avoid vulnerability to UAPs. The vulnerability of UAPs from natural images will become a remarkable security threat.

Download Full-text

Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.8b00350 ◽

2018 ◽

Vol 58 (11) ◽

pp. 2319-2330 ◽

Cited By ~ 33

Author(s):

Fergus Imrie ◽

Anthony R. Bradley ◽

Mihaela van der Schaar ◽

Charlotte M. Deane

Keyword(s):

Neural Networks ◽

Virtual Screening ◽

Transfer Learning ◽

Deep Neural Networks ◽

Protein Family

Download Full-text

Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification

Entropy ◽

10.3390/e21050456 ◽

2019 ◽

Vol 21 (5) ◽

pp. 456 ◽

Cited By ~ 3

Author(s):

Hao Cheng ◽

Dongze Lian ◽

Shenghua Gao ◽

Yanlin Geng

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Model Selection ◽

Image Classification ◽

Transfer Learning ◽

Deep Neural Networks ◽

Classification Problem ◽

Distribution Model ◽

Classification Problems ◽

Information Bottleneck

Inspired by the pioneering work of the information bottleneck (IB) principle for Deep Neural Networks’ (DNNs) analysis, we thoroughly study the relationship among the model accuracy, I ( X ; T ) and I ( T ; Y ) , where I ( X ; T ) and I ( T ; Y ) are the mutual information of DNN’s output T with input X and label Y. Then, we design an information plane-based framework to evaluate the capability of DNNs (including CNNs) for image classification. Instead of each hidden layer’s output, our framework focuses on the model output T. We successfully apply our framework to many application scenarios arising in deep learning and image classification problems, such as image classification with unbalanced data distribution, model selection, and transfer learning. The experimental results verify the effectiveness of the information plane-based framework: Our framework may facilitate a quick model selection and determine the number of samples needed for each class in the unbalanced classification problem. Furthermore, the framework explains the efficiency of transfer learning in the deep learning area.

Download Full-text

Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings

Journal of Cleaner Production ◽

10.1016/j.jclepro.2019.119866 ◽

2020 ◽

Vol 254 ◽

pp. 119866 ◽

Cited By ~ 24

Author(s):

Yujiao Chen ◽

Zheming Tong ◽

Yang Zheng ◽

Holly Samuelson ◽

Leslie Norford

Keyword(s):

Neural Networks ◽

Model Predictive Control ◽

Transfer Learning ◽

Predictive Control ◽

Natural Ventilation ◽

Deep Neural Networks ◽

Smart Buildings

Download Full-text

Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

Complexity ◽

10.1155/2019/2952304 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 7

Author(s):

Alejandro Baldominos ◽

Yago Saez ◽

Pedro Isasi

Keyword(s):

Neural Networks ◽

Evolutionary Computation ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Character Recognition ◽

Deep Neural Networks ◽

Handwriting Recognition ◽

Computational Time ◽

Learning Models ◽

Expensive Process

Neuroevolution is the field of study that uses evolutionary computation in order to optimize certain aspect of the design of neural networks, most often its topology and hyperparameters. The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. In this paper, we rely on previous work to apply neuroevolution in order to optimize the topology of deep neural networks that can be used to solve the problem of handwritten character recognition. Moreover, we take advantage of the fact that evolutionary algorithms optimize a population of candidate solutions, by combining a set of the best evolved models resulting in a committee of convolutional neural networks. This process is enhanced by using specific mechanisms to preserve the diversity of the population. Additionally, in this paper, we address one of the disadvantages of neuroevolution: the process is very expensive in terms of computational time. To lessen this issue, we explore the performance of topology transfer learning: whether the best topology obtained using neuroevolution for a certain domain can be successfully applied to a different domain. By doing so, the expensive process of neuroevolution can be reused to tackle different problems, turning it into a more appealing approach for optimizing the design of neural networks topologies. After evaluating our proposal, results show that both the use of neuroevolved committees and the application of topology transfer learning are successful: committees of convolutional neural networks are able to improve classification results when compared to single models, and topologies learned for one problem can be reused for a different problem and data with a good performance. Additionally, both approaches can be combined by building committees of transferred topologies, and this combination attains results that combine the best of both approaches.

Download Full-text

Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning

Energy ◽

10.1016/j.energy.2020.119356 ◽

2020 ◽

pp. 119356

Author(s):

Xin Liu ◽

Zheming Cao ◽

Zijun Zhang

Keyword(s):

Neural Networks ◽

Wind Turbine ◽

Transfer Learning ◽

Deep Neural Networks ◽

Short Term ◽

Power Outputs

Download Full-text

Fast Adaptation of Thermal Dynamics Model for Predictive Control of HVAC and Natural Ventilation Using Transfer Learning with Deep Neural Networks

2020 American Control Conference (ACC) ◽

10.23919/acc45564.2020.9147321 ◽

2020 ◽

Author(s):

Yujiao Chen ◽

Yang Zheng ◽

Holly Samuelson

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Predictive Control ◽

Natural Ventilation ◽

Deep Neural Networks ◽

Dynamics Model ◽

Thermal Dynamics ◽

Fast Adaptation

Download Full-text

Transfer Learning and Deep Domain Adaptation

Advances and Applications in Deep Learning ◽

10.5772/intechopen.94072 ◽

2020 ◽

Author(s):

Wen Xu ◽

Jing He ◽

Yanfeng Shu

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Transfer Learning ◽

Real World ◽

Deep Neural Networks ◽

Domain Adaptation ◽

Fine Tuning ◽

Real World Applications ◽

Comprehensive Survey ◽

Sample Reconstruction

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.

Download Full-text