Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Finding suitable features has been an essential problem in computer vision.  focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned filters. ,  propose the Explicit Rotation-Invariant Restricted Boltzmann Machine, which exploits prior information coming from the dominant orientation of images. Our model extends the standard RBM, by adding a suitable number of ight matrices, associated with each dominant gradient.  show that our approach is able to learn rotation-invariant features, comparing it with the classic formulation of RBM on the MNIST benchmark dataset. Overall, requiring less hidden units, our method learns compact features, which are robust to rotations.
['Mario Valerio Giuffrida', 'Sotirios A. Tsaftaris']
I am writing a research paper on a idea Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
[{"from": "human", "value": "I am writing a research paper on a idea Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Finding suitable features has been an essential problem in computer vision.  focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned filters. ,  propose the Explicit Rotation-Invariant Restricted Boltzmann Machine, which exploits prior information coming from the dominant orientation of images. Our model extends the standard RBM, by adding a suitable number of ight matrices, associated with each dominant gradient.  show that our approach is able to learn rotation-invariant features, comparing it with the classic formulation of RBM on the MNIST benchmark dataset. Overall, requiring less hidden units, our method learns compact features, which are robust to rotations."}]