Day 25

Today we switched directions a bit and tried the ladder network on SMCAE-generated features. It's performing a little better than the SVM, which is looking promising for my presentation and Ron's paper. We also looked into the SCAE data from a while ago, and it honestly looks pretty bad. The multiloss CAEs aren't performing very well, so we might have to reframe that experiment too. Luckily the CAE vs. MCAE data is still pretty good, so it's not a complete loss.

I also sat in on a DIRS meeting, where Prof. Rottman (from Ben-Gurion University of the Negev) gave a funny and informative presentation on target detection in hyperspectral images. It's an interesting problem, and he explained the mathematics behind it quite well.

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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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