Transferability of Features in Deep Neural - myassignmenthelp

Question: Discuss about theTransferability of Features in Deep NeuralNetworks. Answer: Problem Definition Deep neural networks in the modern world exhibit a curios phenomenon in that when trained with images, they have a tendency to all learn first layer features that are similar to color blobs or Gabor filters. These filters appear so commonly that if anything else is obtained in natural image datasets, the result is a suspicion that the hyper parameters chosen was done poorly or there is a bug in the software. This phenomenon is seen in different datasets as well as where the training objectives are very different including in situations of supervised image classification, unsupervised sparse representations learning, and unsupervised density learning. Regardless of the natural dataset and the specific cost function, the standard features in first layer systems seem to occur and so these features (first features) are considered general. Further, last layer trained network computed features must greatly depend on the chosen task and dataset; the last layer features are thus termed speci fic (Singh et al., 2015). Given that the first layers are general while last layers are specific, then within the network, there must be a point of transition from general to specific (Joshi, 2017). With this in mind, this pre-research proposal has the following objectives; Objectives To quantify the degree to which a specific layer is specific or general To establish whether the transition from general to specific occur suddenly at a singe layer or whether it occurs spread out out in over many layers To establish where the transition occurs; whether it is near the first, the middle, or the last layer in the network Time Table Task Duration/ Time Evaluating research topics and identifying suitable research area Three Days (Nov 25 2017 to Nov 28 2017) Writing preproposal One day (Nov 29 2017) Pre research data and materials collection One Week Writing formal research proposal One Week Getting professor feedback and making necessary adjustments Two Weeks Designing research methodology Three Days Collecting materials for the research One Week Literature Reviews Two Weeks Designing experimental setup One Week Data Collection One Week Data analysis One Week Discussion of research findings Four Days Making Draft Research Five days Obtaining professor feedback Two weeks Making adjustments and writing final research paper with conclusions and recommendations Two weeks Presenting research One day References Joshi, N. (2017). Combinational neural network using Gabor filters for the classification of handwritten digits (pp. 1-4). Frankfurt: Frankfurt Institute for Advanced Studie. Retrieved from https://arxiv.org/pdf/1709.05867.pdf Singh, B., De, S., Zhang, Y., Goldstein, T., Taylor, G., 2015 (December 01, 2015). Layer- Specific Adaptive Learning Rates for Deep Networks. IEEE 14th International Conference on Machine Learning and Applications (ICMLA). 364-368.