정리 카테고리

Lecture 2 formalizes the problem of image classification. We discuss the inherent difficulties of image classification, and introduce data-driven approaches. We discuss two simple data-driven image classification algorithms: K-Nearest Neighbors and Linear Classifiers, and introduce the concepts of hyperparameters and cross-validation.

Keywords: Image classification, K-Nearest Neighbor, distance metrics, hyperparameters, cross-validation, linear classifiers

slides:
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture2.pdf

How do we work on this image classification task?

• system input: images, predermined category labels

• what computer really seas: just a grid

  

Challenges:

our classification algorithm should be robust at different kinds of transforms.

1. Viewpoint variation
2. Illumination
3. Deformation
4. Occulusion
5. Background Clutter
6. Intraclasss variation

Data-Driven Approach

1. Collect a datasset of images, labels
2. Use ML to train a classifier
3. Evaluate the classifier on new images

Classifier

K-Nearest Neighbor

• majority vote among K
• tends to smooth out our decision boundaries and lead to better results.
• L1 distance, L2 distance
  • dependent on problem or data
  • so just reccomend to try them both and see what works better
  • Actually, never used
  1. very slow at test time
  2. Distance metrics on pixels are not informative
  3. Curse of dimensionality

Hyperparamets

• choices about the algorithm that we set rather than learn

Setting Hyperparameters

1. Choose hyperparameters that work best on the data (Don't do this)
   -> K=1 always works perfectly on training data

2. Split data into train and test; choose hyperparameters that work best on test data (Don't do this)
   -> No idea how algorithm will perform on new data

3. Split data into train and val, and test; choose hyperparameters on val and evaluate on test (Better!)

4. Cross-Validation: Split data in folds, try each fold as validation and average the results

Q. training set vs validation set

• algorithm doesn't have direct accesss to the labels of validation set.
• uses validation set only for checking how well algorithm is doing.

Linear Classification