1. Object Classification through Deconvolutional Neural Networks
Student: Carlos Rubiano
Mentor: Oliver Nina

2. Network in Network
Model different from typical CNN that aims to enhance discriminability for local patches in receptive field
Traditional CNN stacks convolutional and pooling layers and then combines layers in fully connected layer at end
Loss of spatial information
Over fitting can occur

3. Network in Network
NIN’s model: Passed through multilayer perceptron and then through stacked ‘mlp’ convolutional layers that have composing elements which represent micro networks
Essentially NIN’s micro networks seperates input image into segments and classifies them individually whereas traditional CNN will look at different level of features in each convolutional layer and then combine the features at the end
Rather than using fully connected layer, NIN uses a global average pooling layer which natively prevents overfitting
arxiv.org/pdf/ v3.pdf

4. Rectifier Linear Unit Standard way to model neuron’s output:
f(x) = tanh(x)
However very slow
Hinton and Nair: f(x) = max(0, x)
Maxout:

5. Feature Map Calculations

6. UT-Interaction Dataset
Dataset with 2 sets of 6 classes with 10 sequences for each class (total of 120 sequences)
Extracted frames from segments
Resized images to fit our network
Calculated the Optical flow
Results
Imagenet: 59%, Current system: 64%

7. What’s Next?
MBH descriptor seperates optical flow into its horizontal and vertical components
Calculated by taking the gradient of the optical flow to get MBHx and MBHy
Implemented NIN, working on testing with it
Combine different ReLU from maxout and NIN