Day 8

Today Joe came back for morning meetings and Nate left to run in the mountains. Ron's supposed to be back tomorrow. Over the weekend, I collected data for the CAE experiments, so I don't have any more work with that until Ron gets back. So, I started looking at the ladder network architecture Ron implemented with TensorFlow. It's probably the most intricate model I've looked at thus far, but like anything else, it slowly made sense as I broke it down. The papers I read earlier helped a lot by providing a general deconstruction of the model. I used these papers, example code, and a healthy dose of common sense to document the code. I had a lot of downtime, so I did some reading and personal experimentation. In particular, I wanted to get familiar with what HSI data looks and feels like. Here's the result of a small script I wrote:
This is a visualization of the 103 bands included in the Pavia University HSI dataset. On the right, brighter colors show higher reflectance. Some casual analysis shows that only a few objects are very reflective in the lower wavelengths (430-680 nm). These wavelengths line up with visual spectrum, which explains why the objects appear white in person (note how the cyan-painted metal reflects less in red wavelengths). In the same range, you can also see how grass and trees reflect only green light. Most noticeably, almost every object in the image "lights up" in wavelengths >700 nm, just outside of the visible spectrum. Shadows stay dark, for obvious reasons. A lot of this is probably pretty obvious, but it's nice to confirm these things with data. Cool stuff, more to come.

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Outline

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

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Day 28

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