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At Oxford University we have been running a Doctoral Training Programme (http://www.lsi.ox.ac.uk/) for 5 years which is a four year programme that takes students from a range of

Transfer learning from natural image datasets, particularly ImageNet, using models (VGG16, DenseNet, GoogLeNet etc.) and cor- responding pretrained weights are used for deep learning

Tech (Computer Science) degree of the Indian Statistical Institute.

The Edge User Allocation (EUA) problem aims to ensure users are allocated edge server resources while satisfying constraints on coverage, resource availability, temporally varying

In this chapter, we propose a hybrid model combining linear autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive neural networks (ARNN) model to take

Proposed Model Architecture 21 The Generated Depth Image is then fed to Discriminator for Probabilistic Analysis of whether it’s a fake or not and the resulted loss is then used to

[11] Very strong rainbow connection number vsrc(G) of a graph G , which is the smallest number of colors for which there exists a coloring of E(G) such that, for every pair of vertices

OPTIMAL SCHEDULING OF TRUCKS FOR THE CLEARANCE OF GARBAGE OF A CITY. A dissertation submitted in partial fulfillment of the requirements for the

Figure 7.1 displays an example input image, it’s weakly labelled ground truth data and the corresponding segmentation obtained from our Deep Learning based classifier. We have

1. Consider an economy consisting of a constant returns sector and an increasing returns sector. Both sectors produce the same good using a single factor of

We saw that, given less number of training set documents at a particular cluster, this algorithm performs very well with the highest accuracy of 86.79% using MI feature selection method

Compared to FCNN RDNet and CNN RDNet, PatchMatch takes much more time to generate the desired images even though all the three methods generate a texture image using a single input

Few-Shot Meta Learners for Domain Adaptation.. DISSERTATION SUBMITTED IN

Learning based relay se- lection [18] provides an interesting framework by which static and dynamic obstacles can be learnt by using the history of mobile communications over the

C is total number of class in the training data and X i is the sparse representation of the i th class specific signal Y i corresponding to complete dictionary D.. However soon after

1 Content analysis of interview responses revealed that students in primary education are motivated to read for three intrinsic motivation (reading for mastery over difficult tasks

9 Medical Image Segmentation using U-Net with MobileNetV2 as encoder 28 9.1 Depthwise separable convolution - Building block of MobileNetV1..

• We have proposed a new model, where we Segment the Fundus images for Diabetic Retinopathic pathologies, and used the Deep Pathological Features learned in the process of segmentation