note

Autencoder
    Basic
        Evaluation Metrics
    Variants of Autoencoder:    
        WAE
        VAE
    Robust
        ECML
        KDD
  
Gans
    Basic
    BiGans

Extending Classical Approach
    SVDD
    OCNN

Real World UseCases
    a) Unlabelled unsupervised ( No Labels)
    b) Unsupervised: Labelled positive data,( Inlyers) (One class labels)

Hands On 
    ECML : Start with standard autoencoder.
           Move foward towards robust autoencoder.
           From Normal to Robust Loss function.
           Data Set examples restaurant.

/Users/cha764/Documents/projects/TransUrban/data/04_06_2020/Measurements/LGR15W




Dear Author,

Please remember to submit "Robust Deep Learning Methods for Anomaly Detection," for publication in the proceedings and ACM DL.

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