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

2. Deep Convolutional Neural Networks have achieved success in large scale object classification

3. Problem
Despite success, issue in way networks learn filters from training data
Large amount of training data risks overfitting
This can imply memorization
Overfitting can be minimized by training different independent models with same/or different parts of training data
Combine classification results from different models

4. Methodology
Bootstrap aggregation to perform uniformly sampling with replacement of a training set D that contains n samples to create d subsets of n' where n ≥ n'
Train n networks with different samples of the dataset and combine results through majority voting, probabilistic averaging and geometric mean
Voting ensemble using same dataset and same amount of data due to randomness of minibatch process for stochastic gradient descent in DCNN

5. Preliminary Testings

6. Additional Results
Kaggle, world's largest community of data scientists
Currently holding a CIFAR-10 Object Recognition competition
Applied our methodology and submitted results to competition
Achieved 7th/155

7. What's next? Also exploring other ways for data augmentation
Such as sampling with custom features and image variants
Implement state of art Network in Network and combine with maxout and our methodology