FloodCam-WUSFeats

• Daniel Buscombe, Marda Science -

Flooded Road Detector using (weakly-/un-)supervised feature extraction, and uncoupled classification

Supervised Deep Learning

In the companion repository, NC-TrafficCams, we have implemented a supervised deep neural network that couples feature extraction (using CNNs) explicitly with classes during the training process. In summsry:

• It uses mobilenet feature extraction with distillation head (max pool), and classifying head (dense layer with dropout and kernel regularization),
• It is retrained with data but feature extractor layers keep imagenet weights
• It has no supervised feature extraction when imagenet weights are used and frozen, but there is explicit supervised mapping of those features to classes by iteratively adjusting a model to do so
• It has enormous number of parameters, but only classification parameters tuned if used in transfer learning mode

Weakly Supervised Deep Learning

Here I explore two alternatives to the above, that relax the level of supervision in the hope of creating a good alternative model; one that might even be more portable to sites and times and conditions outside those represented in training. It's also a good idea to use multiple indepedent models for ensemble or consensus approaches that might result in more robust predictions.

Model 1

supervised "uncoupled" classification from unsupervised feature extraction (baseline method)

• extract 100 principal components, then kNN classification
• lowest amount of supervision; no supervised feature extraction, no explicit coupling of features to classes (kNN functions like a lookup table)

Model 2

supervised "uncoupled" classification from features from weakly supervised feature extraction

• use convolution layers, global pooling, and dense layer with no activation to create embeddings from images
• weight the convolution layers using a loss function that just positions embeddings in embedding space such that embeddings are more similar to similar classes than different classes, so a very minimal bar
• (embeddings different from feature vectors becos dont change size with different sized images)
• medium supervision; weakly supervised feature extraction, no explicit coupling of features to classes (kNN functions like a lookup table)
• relative few parameters (~300,000), scales with convolution layer sizes