1. Hierarchical Deep Convolutional Neural Network
Justin Essert

2. Motivation Convolutional Neural Networks
Widespread standard for image recognition
Weeks to train
Struggle on sparse data

3. Dataset – Cifar100 50,000 32x32 training images
Low resolution
Fewer samples per category than many other datasets
State-of-the-art: 30-40% error rates
100 categories (fine) – 20 super-categories (coarse)
Superclasses
Classes
Aquatic Mammals
Beaver, Dolphin, Otter, Seal, Whale
Flowers
Orchids, Poppies, Roses, Sunflowers, Tulips
Household Furniture
Bed, Chair, Couch, Table, Wardrobe

4. Convolutional Neural Networks – VGG16
Simonyan, Karen & Zisserman, Andrew. Very Deep Convolutional Networks For Large-Scale Image Recognition. arXiv: , 2015
Pre-trained on ImageNet (transfer learning)
Photo Credit: Heuritech Le Blog

5. Hierarchical Deep CNN Initial network is trained on entire dataset
Yan, Zhicheng et al. HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition. arXiv: , 2014
Initial network is trained on entire dataset
Fine -> coarse prediction
20 ‘Fine’ networks (for each superclass)
Final prediction = weighted sum of fine networks

6. Hierarchical Deep CNN (Example)
Dataset:
Superclasses
Classes
Aquatic Mammals
Beaver, Otter
Household Furniture
Bed, Wardrobe
So lets look at the output of our trained network when we pass in a picture of a wardrobe!

7. Hierarchical Deep CNN (Example)
Coarse Classifier:
Labels:
Probability:
Aquatic Mammals
0.10
Household Furniture
0.90
1st Fine Classifier:
Labels:
Probability:
Beaver
0.50
Otter
2nd Fine Classifier:
Labels:
Probability:
Bed
0.00
Wardrobe
1.00

8. Hierarchical Deep CNN (Example)
Coarse Classifier:
Final Classification:
Labels:
Probability:
Aquatic Mammals
0.10
Household Furniture
0.90
Labels:
Probability:
Beaver
0.10*0.50 = 0.05
Otter
Bed
0.90*0.00 = 0.00
Wardrobe
0.90*1.00 = 0.90
1st Fine Classifier:
Labels:
Probability:
Beaver
0.50
Otter
2nd Fine Classifier:
Labels:
Probability:
Bed
0.00
Wardrobe
1.00

9. Questions?