Back in around 2008, SVMs were all the rage in computer vision. We would use hand designed visual features and then a linear SVM on top. That was how object detectors were built (remember DPM?)
Funny how SVMs are just max-margin loss functions and we just took for granted that you needed domain expertise to craft features like HOG/SIFT by hand.
By 2018, we use ConvNets to learn BOTH the features and the classifier. In fact, it’s hard to separate where the features end and the classifier begins (in a modern CNN).
Funny how SVMs are just max-margin loss functions and we just took for granted that you needed domain expertise to craft features like HOG/SIFT by hand.
By 2018, we use ConvNets to learn BOTH the features and the classifier. In fact, it’s hard to separate where the features end and the classifier begins (in a modern CNN).