Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation.  show, hover, that standard Knowledge Distillation is not effective for learning small models for the task of pedestrian detection. To improve this process,  introduce a higher-dimensional hint layer to increase information flow.  also estimate the variance in the outputs of the large network and propose a loss function to incorporate this uncertainty. Finally,  attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection.  succeed in training a model that contains $400\times$ fer parameters than the large network while outperforming AlexNet on the Caltech Pedestrian Dataset.
['Jonathan Shen', 'Noranart Vesdapunt', 'Vishnu Naresh Boddeti', 'Kris M. Kitani']
I am writing a research paper on a idea In Teacher We Trust: Learning Compressed Models for Pedestrian Detection
[{"from": "human", "value": "I am writing a research paper on a idea In Teacher We Trust: Learning Compressed Models for Pedestrian Detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation.  show, hover, that standard Knowledge Distillation is not effective for learning small models for the task of pedestrian detection. To improve this process,  introduce a higher-dimensional hint layer to increase information flow.  also estimate the variance in the outputs of the large network and propose a loss function to incorporate this uncertainty. Finally,  attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection.  succeed in training a model that contains $400\\times$ fer parameters than the large network while outperforming AlexNet on the Caltech Pedestrian Dataset."}]