Thinking@Scale Yan Qi     About     Feed

Met Neural Network

When I was in college, I had a chance of working with a post-doc in our department on the contented-based image retrieval (CBIR)1. Clearly it was a hard problem. For example, not like text, it is challenging to capture the user real intention. Moreover the image object recognition has been always an open problem. Therefore, we proposed a different approach from the traditional submit-question-return-answer search strategy. It is an interactive retrieval. In other words, it might not be able to return the satisfactory result first, instead it invites the user to give its feedback if the result is not perfect. An improved result will be given in the following taking the user feedback into account, until the user gets the right image, conceptually. The tools serving our purpose included the BP neural network and the interactive genetic algorithm (IGA)2.

Then ImageNet didn’t exist in those years, so in our experiments, we had to crawl many images from the Internet. Whereas the variety was a problem as most of the images we got were landscape photos. You can image that the quality of our work was kind of limited. However, working on this project was really an inspiring experience to me, as it opened a door to an unknown world where I had never been.

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Another interesting project that I got involved in my graduate school in the USTC was to create a bridge health monitor system. My advisor, Professor Lu led the effort to the software development. Tentatively I created a BP neural network to predict the bridge health 3. However, the performance was not good enough for the real-life application. On one hand, if the training data were not chosen properly, the result would not be right. In my experiment, there were often no enough data for the training. On the other hand, the training process was always time consuming, and not very effective.

I started realizing that the neural network might not be as effective or efficient as it does sound. It tries to simulate the way people think, but clearly there is still a long way to go before it can think like a man.