[PDF] Top 20 Deep Kernel based Convolutional Neural Networks for Image Recognition

... for image recognition requires deep machine ...on convolutional neural net processor as the specific task requires massive amount power for its compute-intensive nature images are often ... See full document

... Abstract. Deep convolutional neural networks (CNN) have recently proven extremely competitive in challenging visible light image and speech recognition ...learned ... See full document

... lower recognition rate or more time- ...original image as the input of network directly, avoiding the manual extraction of features that would lead to the accumulation of ... See full document

... problem. Deep CNNs have led to a series of breakthroughs in image recognition and associated tasks for visual identification of ...enhance image recognition ...sufficiently deep, ... See full document

... 2012). Convolutional neural networks have only recently been ap- plied to automatic classification of wildlife images, with limitations in performance ...same image recognition ... See full document

... new image pre-processing method, which can show features or important information ...clearly. Deep learning methods have grown rapidly in the last ten years and have better performance than the traditional ... See full document

... medical image repositories is increasing ...content based medical image retrieval (CBMIR) ...Using deep convolution neural network (CNN) to construct the CBMIR system can fully ... See full document

... the deep learning methods avoid feature engineering in supervised learning ...data, deep learning algorithms can be applied to such kind of ...The deep belief networks are the example of ... See full document

... using deep convolutional neural networks (CNNs). Deep CNNs have achieved a great success in acoustic modelling for automatic speech recognition due to its ability of reducing ... See full document

... medical image processing for diagnosis of bladder cancer through cystoscopy images despite the high prevalence in the ...well-known convolutional neural networks (CNNs) and a multilayer ... See full document

... action recognition approaches root in powerful learning methods, particulary the deep CNN approaches [25], ...existing deep models to learn action representations from videos are categorized as four ... See full document

... Previous research [9, 10] shows that without carefully measuring the distribution similarity between the source and target data, the source knowledge could not be exploited effectively or even hurt the learning process ... See full document

... of deep learning methods. With the advent of large scale image dataset ImageNet (Russakovsky et ...cellent deep learning architectures have been proposed, such as VGG (Simonyan and Zisserman 2014), ... See full document

... the convolutional layers merely as a feature ...well-known deep convolutional neural network framework known as residual learning (ResNet) ...deeper networks and are a great candidate ... See full document

... clothing image plays an important role in the recommendation system for clothing based on expert ...style based on the experience of the expert in fashion image design is complex taking the ... See full document

... Traditional image processing techniques provided reasonable outcomes and performance regarding insect pest detection using insect ...As deep learning has revolutionized the area of computer vision ... See full document

... techniques based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of ... See full document

... removal based on DCNN can effectively removes any kind of eyeglasses no matter the eyeglasses cover eyes or not, which can keep the stability of face recognition under ... See full document

... several neural network architectures such as probabilistic neural nets in combination with Concordia patterns [1] and multi-resolution analysis using wavelets for power system fault feature extraction has ... See full document

... fully convolutional underlying ...entire image at once, YOLO provides as advantage over other systems since it is essentially a single CNN performing both the tasks of bounding box prediction and ... See full document