Deep Learning and its Applications - Computer Vision Zipfian Academy Meetup Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices. The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.

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Deep Learning
And Its Applications: Computer Vision
Adam Gibson
{ deeplearning4j.org // skymind.io // zipfian academy

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• Object Recognition
• Image Categorization
• Scene Parsing
• Face Recognition
Computer Vision: A Primer

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• OpenCV
• SIFT
• Filters/Edge Detection
• Feature Extraction
What’s currently done?

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• Representation Learning
• More precise than hand-­‐‑done
features
• Non-­‐‑linearities and higher-­‐‑order
trends
• Pretrain and Hessian Free
This is manual!

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• Representation Learning
• Position Invariance with convolutions
• Semantic Hashing
Deep Learning and Images

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• Normal pixels – 0-­‐‑255 –
normalization
• Sparse – binarization (depending on
pixel presence)
Different kinds of images

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• Faces = a collection of images.
• With persistent pa_erns of pixels.
• Pixel pa_erns = features.
• Nets learn to identify features in data, to
classify faces as faces and label them: John or
Sarah.
• Nets train by reconstructing faces from features
many times.
• Measuring their work against a benchmark.
Facial recognition

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DL4J’s Facial Reconstructions

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• Slices of a feature space (Max pooling)
• Learns different portions for easily scalable
and robust feature engineering.
Position Invariance -­‐‑ Convolutions

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Visual Example -­‐‑ Convolutions

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Pen Strokes

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• Facebook uses facial recognition to make
itself stickier and know more about us.
• Government agencies use it to secure
national borders.
• Video game makers use it to construct more
realistic worlds.
• Stores use it to identify customers and track
behavior.
What are faces for?

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• 2 layers of neuron-­‐‑like nodes.
• The 1st is the visible, or input, layer
• The 2nd is “hidden.” It identifies features in input
• Symmetrically connected.
• “Restricted” = no visible-­‐‑visible or hidden-­‐‑hidden
ties
• All connections happen between layers.
Restricted Bolgmann
Machines (RBMs)

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• A stack of RBMs.
• Each RBM’s hidden layer à Next RBM’s visible/input
layer.
• DBNs learn more & more complex features
• Example:
• 1) Pixels = input;
• 2) H1 learns an edge or line;
• 3) H2 learns a corner or set of lines;
• 4) H3 learns two groups of lines forming an object
-­‐‑-­‐‑ a face!
• Final layer classifies feature groups: sunset, elephant,
flower, John, Sarah.
Deep-­‐‑Belief Net (DBN)

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• 2 DBNs.
• 1st DBN *encodes* data into vector of 10-­‐‑30
numbers = Pre-­‐‑training.
• 2nd DBN decodes data into original state.
• Backprop only happens on 2nd DBN
• 2nd is the fine-­‐‑tuning stage (reconstruction entropy).
• Reduces documents or images to compact vectors .
• Useful in search, QA and information retrieval.
Deep Autoencoder

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Deep Autoencoder Architecture

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Image Search Results

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• Top-­‐‑down & hierarchical rather than feed-­‐‑forward (DBNs).
• Handles sequence-­‐‑based classification, windows of several
events, entire scenes (multiple objects).
• Features themselves are vectors.
• A tensor = a multi-­‐‑dimensional matrix, or multiple matrices of
the same size.
Recursive Neural Tensor Net

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RNTNs & Scene Composition