Today I presented everything I did over the summer. I felt that all of the presentations went great and I am very grateful to RIT as well as my mentors for this amazing experience. Here is my presentation.
Today I read some academic journals in order to gain a better understanding of lifelong learning. The problem lifelong learning hopes to solve is something called catastrophic forgetting. Catastrophic forgetting is a term used to describe the inability of a neural network to to remember data it is given after training. For a network that needs to continually update itself catastrophic forgetting is a large problem. One approach lifelong learning algorithms take is to continual train on the new and old data in order to maintain its effectiveness on its training data. This approach is not very memory efficient. As the week goes on I plan to learn more about lifelong learning.
At the start of today I watched 2 of Stanford's lectures one on feed-forward neural networks and the other on convolutional neural networks. In a feed-forward network there is no connection to previous nodes. This is a great visualization of the relationship between nodes and how a previous layer provides input for a future layer. The other type of neural network is a recurrent neural network. What makes a recurrent network different is that outputs on a certain layer can act as inputs for the previous layer. I have not gone into depth on recurrent networks so my knowledge is limited. A convolutional network is not another type of network but rather a specific layer on a neural network which alters the data. One example of convolution is pooling. As demonstrated below pooling takes a filter full of values and then averages or sums them creating a new array. Pooling is useful because it reduces the size of your matrix while retaining a similar value. This allows for calculations to...
Today I created an evaluation function for my VQA model. Every loop over the data set I would print the average loss and the overall accuracy. Loss measure the incorrectness of a functions guess. Lower loss is good higher is bad. My function trended toward lower loss as it continued to train on the dataset. I was getting an accuracy of around 30% which is fairly good considering I was training the model on questions that had no associated images. In the future I plan to combine the image data with the question data.
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