Andrew Ng Feature learning for image classification Kai Yu and Andrew Ng

Andrew Ng Computer vision is hard

Andrew Ng Machine learning and feature representations Input Input space Motorbikes “Non”-Motorbikes Learning algorithm pixel 1 pixel 2 pixel 1 pixel 2

Andrew Ng Machine learning and feature representations Input Input space Feature space Motorbikes “Non”-Motorbikes Feature representation Learning algorithm pixel 1 pixel 2 “wheel” “handle” handle wheel

Andrew Ng How is computer perception done? Image Low-level vision features Recognition Image Grasp point Low-level features Low-level state features Action Helicopter Audio Low-level audio features Speaker identification Object detection Audio classification Helicopter control

Andrew Ng Learning representations Sensor Learning algorithm Feature Representation

Andrew Ng Computer vision features SIFT Spin image HoG RIFT Textons GLOH

Andrew Ng Audio features ZCR Spectrogram MFCC Rolloff Flux Problems of hand-tuned features 1. Needs expert knowledge 2. Time-consuming and expensive 3. Does not generalize to other domains

Andrew Ng Computer vision is more than pictures Camera array 3d range scan (laser scanner) 3d range scans (flash lidar) Audio Can we automatically learn good feature representations? Images Visible light image Thermal Infrared Key question: Can we automatically learn a good feature representation? Video

Andrew Ng Learning representations Sensor Learning algorithm Feature Representation

Andrew Ng Sensor representation in the brain [BrainPort; Martinez et al; Roe et al.] Seeing with your tongue Human echolocation (sonar) Auditory cortex learns to see. Auditory Cortex

Andrew Ng Unsupervised feature learning Find a better way to represent images than pixels.

Andrew Ng The goal of Unsupervised Feature Learning Unlabeled images Learning algorithm Feature representation

Andrew Ng Tutorial outline 1. Current methods. 2. Sparse coding for feature learning. — Break — 3. Advanced classification. 4. Advanced concepts & deep learning.