Day 23

More noodling around with the ladder network today.

Ron was helping out, and he put in PELU activation and weight decay to see if they yielded better performance. It hasn't been going very well; I think our highest accuracy is around 82%, which is still significantly lower than a standard SVM-RBF. On top of that, the model is still severely overfitting. While running tests, I worked on my presentation, which is coming along pretty well. I also didn't notice how close our last day was until today. I hope I'm able to get some good results before presentation day rolls around.

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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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