Stanfoard CS231n 2017를 요약한 포스팅입니다. 정보 전달보다 자신을 위한 정리 목적이 강한 글입니다! :)

Image Classification

• Computer Vision의 핵심 Task
  • Input : Image
  • Output : Category Labels
• Semantic Gap
  • 이미지에서 추출할 수 있는 정보(색, 질감)와 사람들이 원하는 추상적 정보의 차이
• Challenges
  • Viewpoint Variation ( 보는 각도 )
  • Illumination ( 조명 )
  • Deformation ( 변형 )
  • Occlusion ( 은폐, 숨김 )
  • Background Clutter ( 배경과 섞임 )
  • Intraclass Variation ( 물체의 다양성 )
• 상상하는 모든 이미지를 실시간으로 판단하고 싶음
• Attempts
  • Find edges
  • Find corners
  • brittle

Data-Driven Approach

• Collect a dataset of images and labels
• Use Maching Learning to train a classifier
• Evaluate the classifier on new images

Classifier : Nearest Neighbor

• Train : Memorize all data and labels
• Predict : Predict the label of the most similiar training image

Example Dataset: CIFAR10

• 10 classes(airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
• 50000 training images
• 10,000 testing images

Distance Metric

• L1(Manhattan) distance
• L2(Euclidean) distance

Hyper Paramter

• What is the best value of k to use?
• Whate is the best distinct to use?
• Choices about the algorithm that we set rather than learn
• Setting Hyper Paramters
  • Idea 1. Choose hyper paramters that work best on your data
    • Bad : \(K=1\) always work perfectly on training data
  • Idea 2. Split data into train and test, choose hyper parameters that work best on test data
    • Bad : No idea how algorithm will perform on new data
  • Idea 3. Split data into train, val, and test; choose hyper parameters on val and evaluate on test
    • Better!
    • validation set : check accuracy, check how well algorithm is doing
  • idea 4. Cross-Validation: Split data into folds, try each fold as validation and average the results
    • Useful for small datasets, but not used too frequently in deep learning

KNN on images never used

• Very slow at test time
• Distance metrics on pixels are not informative
• Curse of dimensionality ( 차원의 저주 )

Parametric Approach

• Image : Array of \(32\times 32\times 3\) (3072)
• Function : \(f(x,W)\)
• Output : 10 numbers giving class scores

Coming up

• Loss function : quantifying what it means to have a “good” W
• Optimization : start with random W and ifnd a W that minimizes the loss
• ConvNets : tweak the functional form of f

Reference

• Stanfoard CS231n 2017

카일스쿨 유튜브 채널을 만들었습니다. 데이터 분석, 커리어에 대한 내용을 공유드릴 예정입니다.

PM을 위한 데이터 리터러시 강의를 만들었습니다. 문제 정의, 지표, 실험 설계, 문화 만들기, 로그 설계, 회고 등을 담은 강의입니다

이 글이 도움이 되셨거나 의견이 있으시면 댓글 남겨주셔요.