#Xdream evolving images for visual skin#

In that process, we searched for the best performing number of layers to unfreeze during transfer learning fine-tuning for each of the CNNs.

#Xdream evolving images for visual skin#

Second, to improve the performance of the CNNs, we used transfer learning from ImageNet pre-trained models as well as pre-trained the CNNs with the skin lesion dataset 'Human Against Machine with 10000 training images (HAM1000)'. First, we benchmarked this dataset for twenty-three well-known CNN architectures in terms of predictive performance metrics, computational complexity metrics, and statistical significance tests.

This dataset was labeled by expert dermatologists and infectiologists from CF-CHU. CF-CHU collected the images from several hospitals in France. In this study, we utilized an EM dataset consisting of images collected from Clermont-Ferrand University Hospital Center (CF-CHU) of France and the internet. There is no publicly available EM image dataset for Lyme disease prediction mainly because of privacy concerns. The main objective of this study is to extensively analyze the effectiveness of CNNs for diagnosing Lyme disease from images and to find out the best CNN architecture for the purpose. Recent studies show that convolutional neural networks (CNNs) perform very well to identify skin lesions from the image but, there is not much work for Lyme disease prediction from EM lesion images. Better diagnosis of these early forms would allow improving the prognosis by preventing the transition to a severe late form thanks to appropriate antibiotic therapy. In the early stage, the disease manifests itself in most cases with erythema migrans (EM) skin lesions. Lyme disease is one of the most common infectious vector-borne diseases in the world. Besides, PIN makes great CTR improvement (relatively 34.67%) in online A/B test. Extensive experiments on 4 industrial datasets and 1 contest dataset demonstrate that our models consistently outperform 8 baselines on both AUC and log loss. Generalizing the kernel product to a net-in-net architecture, we further propose Product-network In Network (PIN) which can generalize previous models. Then we discuss an insensitive gradient issue in DNN-based models and propose Product-based Neural Network (PNN) which adopts a feature extractor to explore feature interactions. We first analyze a coupled gradient issue in latent vector-based models and propose kernel product to learn field-aware feature interactions. In this paper, we study user response prediction in the scenario of click prediction. Recently, deep neural networks have attracted research attention on such a problem for their high capacity and end-to-end training scheme. Due to the sparsity problems in representation and optimization, most research focuses on feature engineering and shallow modeling. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding.

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. All methods are tested on random and real datasets and our results indicate that all models can often compete with or even outperform classical DNNs on small network architectures confirming the viability for applications having restricted memory or computing power. Finally for the first time a robust model is presented which enforces robustness of the BDNN during training. Afterwards all data points in a given subset are forced to follow the same activation pattern, which leads to a much smaller number of integer variables in the mixed-integer programming formulation and therefore to computational improvements. Furthermore to improve efficiency we present an iterative data-splitting heuristic which iteratively splits the training set into smaller subsets by using the k-mean method. Additionally, a local search heuristic is presented to calculate locally optimal networks. We show that the BDNN can be reformulated as a mixed-integer linear program with bounded weight space which can be solved to global optimality by classical mixed-integer programming solvers. In this work we study deep neural networks with binary activation functions and continuous or integer weights (BDNN). Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and computational demands.