R-CNN

Overview Regions with CNN features: Efficient Graph Based Image Segmentation use disjoint set to speed up merge operation Selective Search HOG (Histogram of Oriented Gradient) Multiple criterions (color, texture, size, shape) to merge regions AlexNet/VGG16 R-CNN Notice that many descriptions are replicated from the orignal sources directly. Some Fundermental Conceptions Batch Size Stochastic Gradient Descent. Batch Size = 1 Batch Gradient Descent. Batch Size = Size of Training Set Mini-Batch Gradient Descent. 1 < Batch Size < Size of Training Set Regularization A regression model that uses L1 Regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. Ridge Regularization Ridge regression adds "squared magnitude" of coefficient as penalty term to the loss function. The first sum is an example of loss function. Lasso Regularization Lasso Regression (Least Absolute Shrinkage and Selection Operator) adds "absolute value of magnitude" of coefficient as penalty term to the loss function.