Day 27

Nate and I ran our presentations today in front of the lab and got some feedback. I was worried about going over 10 minutes so I rushed the presentation and ended much quicker than 10 minutes. It was pretty sloppy too, but now I have a better idea of what needs to get changed. It should go much smoother on Monday.

The ladder network tests were running overnight, and didn't finish by the time I came in today. In fact, as of the time I left today, they still didn't finish. I think they should be done Saturday morning, at which point I'll start some new tests.

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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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