Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract:  is focused on studying the view-manifold structure in the feature spaces implied by the different layers of Convolutional Neural Networks (CNN). There are several questions that this paper aims to ansr: Does the learned CNN representation achieve viewpoint invariance? How does it achieve viewpoint invariance? Is it achieved by collapsing the view manifolds, or separating them while preserving them? At which layer is view invariance achieved? How can the structure of the view manifold at each layer of a deep convolutional neural network be quantified experimentally? How does fine-tuning of a pre-trained CNN on a multi-view dataset affect the representation at each layer of the network? In order to ansr these questions  propose a methodology to quantify the deformation and degeneracy of view manifolds in CNN layers.  apply this methodology and report interesting results in this paper that ansr the aforementioned questions.
['Amr Bakry', 'Mohamed Elhoseiny', 'Tarek El-Gaaly', 'Ahmed M. Elgammal']
I am writing a research paper on a idea Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
[{"from": "human", "value": "I am writing a research paper on a idea Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract:  is focused on studying the view-manifold structure in the feature spaces implied by the different layers of Convolutional Neural Networks (CNN). There are several questions that this paper aims to ansr: Does the learned CNN representation achieve viewpoint invariance? How does it achieve viewpoint invariance? Is it achieved by collapsing the view manifolds, or separating them while preserving them? At which layer is view invariance achieved? How can the structure of the view manifold at each layer of a deep convolutional neural network be quantified experimentally? How does fine-tuning of a pre-trained CNN on a multi-view dataset affect the representation at each layer of the network? In order to ansr these questions  propose a methodology to quantify the deformation and degeneracy of view manifolds in CNN layers.  apply this methodology and report interesting results in this paper that ansr the aforementioned questions."}]