Day 14

"Happiness is when the code works."
- poster from the lab
I have finally made some progress on the ladder network. With the code from last time, I wrote a method to pass the Pavia University data into the ladder network. There were a surprising number of difficulties, but I got it to work. There were a few problems with how I formatted the data that I didn't notice at first, so I was heartbroken when the network made abysmal predictions. Turns out these algorithms don't learn too well when fed garbage, so I fixed the data and kept trying. Here is the end result.
The ladder network was trained on 450 labeled HSI samples and 42,776 unlabeled samples trained over 30 epochs. I'll admit the results aren't great (an SVM achieved 84% on similar data). However, only necessary changes were made to the network, so this is basically the same model that was predicting digits yesterday. There's still room for optimization with this model, so I expect the accuracy to be able to get even higher. Things are starting to pick up.

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Motivation/Background: HSI classification What is HSI? Comparison to RGB The advantages of HSI over other kinds of data Instant, remote, nondestructive data collection Hundreds of data points per pixel What is classification? Introduce using toy data set, like CUB-200 Applications of HSI classification Cancer detection Art authentication Food quality analysis Remote sensing Urban development agricultural monitoring environmental assessment Problems faced High dimensionality Scarcity of labeled data Method: MCAE and Ladder Network Feature Extractors Baseline: PCA Inspiration: Autoencoder Our proposed method: MCAE Classifiers Baseline: SVM Inspiration: Neural network Our proposed method: Ladder network Results : State-of-the-art Comparison Comparison of MCAE against PCA and state-of-the-art Comparison of Ladder Network against SVM and state-of-the-art
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Day 2

Day 2 started with a very brief meeting where we went over the schedule for the next six weeks. Abstract, outline, presentation; two weeks per task. It was at this point that I realized the difficulty of making technical topics sound interesting, then I immediately dismissed the problem as one for my future self. Today was dedicated to Support Vector Machine (SVM) creation, evaluation, and optimization. I was unaware of many of the wonderful functions included in the provided libraries, and ended up doing the data reformatting less elegantly. It still worked, so I left it. When I finished up my SVM code, I tried to help Nate with the errors he was running into. The keyword here is "tried." Note that it's not easy to explain concepts that you figured out hours earlier and happen to be more complicated than they should be. Nate was a good sport about it though, and with Ron's help, he had a good idea of the solution by the end of the day. For the last part of the day,
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Day 28

Week 6 of the internship; the final presentation is on Thursday. It doesn't feel like week 6, but that's probably because I missed almost all of week 4. Regardless, there was much work to do before Thursday, so Nate and I rehearsed our presentations twice in front of the lab. Many revisions were made, and I'm feeling much more prepared for the real deal. Thank you to everybody who was willing to listen to four lame high school presentations about the same topic in the same day. Your contribution will make the very same presentations significantly less lame on Thursday. Apart from presentation-related stuff, Ron and I ran some ladder network and SMCAE experiments all day. Ron tackled the ladder network with PELU activation (which was apparently very difficult) and I continued with adjusting initial layer sizes on the ladder network and doing SMCAE runs with fixed component count PCA as a feature scaler. The results aren't looking too hot, but I'll withhold judgem
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