Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features. While sparse coding has become an increasingly popular method for learning visual features, it is most often trained at the patch level. Applying the resulting filters convolutionally results in highly redundant codes because overlapping patches are encoded in isolation. By training convolutionally over large image windows, our method reduces the redudancy beten feature vectors at neighboring locations and improves the efficiency of the overall representation. In addition to a linear decoder that reconstructs the image from sparse features, our method trains an efficient feed-forward encoder that predicts quasi-sparse features from the input. While patch-based training rarely produces anything but oriented edge detectors,  show that convolutional training produces highly diverse filters, including center-surround filters, corner detectors, cross detectors, and oriented grating detectors.  show that using these filters in multistage convolutional network architecture improves performance on a number of visual recognition and detection tasks.
['Koray Kavukcuoglu', 'Pierre Sermanet', 'Y-Lan Boureau', 'Karol Gregor', 'Michael Mathieu', 'Yann Le Cun']
I am writing a research paper on a idea Learning Convolutional Feature Hierarchies for Visual Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Learning Convolutional Feature Hierarchies for Visual Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features. While sparse coding has become an increasingly popular method for learning visual features, it is most often trained at the patch level. Applying the resulting filters convolutionally results in highly redundant codes because overlapping patches are encoded in isolation. By training convolutionally over large image windows, our method reduces the redudancy beten feature vectors at neighboring locations and improves the efficiency of the overall representation. In addition to a linear decoder that reconstructs the image from sparse features, our method trains an efficient feed-forward encoder that predicts quasi-sparse features from the input. While patch-based training rarely produces anything but oriented edge detectors,  show that convolutional training produces highly diverse filters, including center-surround filters, corner detectors, cross detectors, and oriented grating detectors.  show that using these filters in multistage convolutional network architecture improves performance on a number of visual recognition and detection tasks."}]