Appendix Classification - AsyDynamics/CS231n GitHub Wiki
This is the notes about Image Classification, extra material for CS231n course.
Source: http://cs231n.github.io/classification/
Challenge
- challenge
- Viewpoint validation
- Scale variation
- Deformation
- Occlusion - Sometimes only a small portion of an object (as little as few pixels) could be visible
- Illumination condition
- Background clutter
- Intra-class variation - different type
- pipeline
- input - N images, labeled with K class
- learning - training a classifier, or learning a model.
- evaluation
Nearest Neighbor Classifier
It's nothing to do with Convolutional Neural Networks.
Dataset CIFAR-10
-
L1 distance
$ d=sum(I_1-I_2) $ -
L2 distance - euclidean distance $ d=sqrt(sum(I1^2-I2^2)) $
k - Nearest Neighbor Classifier
Instead of finding the single closest image in the training set, find the k top closest images and have vote on the label of the test image. Higher K have a smoothing effect that makes the classifier more resistant.
Validation sets for hyper-parameter tuning
- Validation set
- Cross-validation