[学习笔记] CS131 Computer Vision: Foundations and Applications（Lecture 9 深度学习2） _applications

冲天香阵透长安，满城尽带黄金甲。这篇文章主要讲述[学习笔记] CS131 Computer Vision: Foundations and Applications：Lecture 9 深度学习2相关的知识，希望能为你提供帮助。
深度学习 So far this week

Edge detection
RANSAC
SIFT
K-Means
Linear classifier
Mean-shift
PCA/Eigenfaces
Image features

Current Research【[学习笔记] CS131 Computer Vision: Foundations and Applications（Lecture 9 深度学习2）】

文章图片

Learning hierarchical representations from data
End-to-end learning: raw inputs to predictions
can use a small set of simple tools to solve many problems
has led to rapid progress on many problems
Inspired by the brain(very loosely!)

Deep learning for different problems vision tasks

visual recognition

文章图片

文章图片
object detection: what and where

文章图片
object segmentation
image caption
visual question answering
super resolution
image retrieval
style transfer

outside vision tasks

Machine Translation
Text Synthesis
Speech Recognition
Speech Synthesis

MotivationData-driven approach:

collect a dataset of images and labels
use machine learning to train an image calssifier
evaluate the classifier on a withheld set of test images

文章图片

feature learning
what is feature learning?[^what is feature learning]

文章图片

deep learning

文章图片

Supervised learning

文章图片

linear regression

文章图片

neural network

文章图片

neural networks with many layers

文章图片

Gradient descent how to find the best weights \\(w^T\\)

文章图片

which way is down hill?

文章图片

gradient descent
fancier rules:

Momentum
NAG
Adagrad
Adadelta
Rmsprop

文章图片

文章图片

这里以后可以再看看!
Backpropagation

文章图片

a two-layer neural network in 25 lines of code

import numpy as np D,H,N = 8, 64,32 #randomly initialize weights W1 = np.random.randn(D,H) W2 = np.random.randn(H,D) for t in xrange(10000): x = np.random.randn(N,D) y = np.sin(x) s = x.dot(W1) a = np.maxium(s,0) y_hat = a.dot(W2) loss = 0.5*np.sum((y_hat-y)**2.0) dy_hat = y_hat - y dW2 = a.T.dot(W2.T) da = dy_hat.dot(W2.T) ds = (s > 0)*da dW1 = x.T.dot(ds) W1 -= learning_rate*dW1 W2 -= learning_rate*dW2

[^what is feature learning]:
In Machine Learning, feature learning or representation learningis a set of techniques that learn a feature: a transformation of raw data input to a representation that can be effectively exploited in machine learning tasks. This obviates manual feature engineering, which is otherwise necessary, and allows a machine to both learn at a specific task (using the features) and learn the features themselves.
Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor measurement is usually complex, redundant, and highly variable. Thus, it is necessary to discover useful features or representations from raw data. Traditional hand-crafted features often require expensive human labor and often rely on expert knowledge. Also, they normally do not generalize well. This motivates the design of efficient feature learning techniques, to automate and generalize this.
Feature learning can be divided into two categories: supervised and unsupervised feature learning, analogous to these categories in machine learning generally.
In supervised feature learning, features are learned with labeled input data. Examples include Supervised Neural Networks, Multilayer Perceptron, and (supervised) dictionary Learning.
In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, and various forms of clustering.