A good summary of criticisms of Deep Learning for Vision (https://t.co/TCcC6cxDCq). IMO one overarching issue is that research is done to beat benchmarks and publish papers, rarely “regularized” by real-world problems with data characteristics that may be significantly different.
— Denny Britz (@dennybritz) February 11, 2019
After a few years of deep learning, “unconventional” bag-of-features techniques are back: https://t.co/BHACpooEme
— hardmaru (@hardmaru) February 11, 2019
Well, Facebook uses ResNet-like ConvNets for all its image recognition (2 to 3 billion photos per day for the Blue Site alone, processed by a handful of ConvNets). The ConvNets are pretrained on billions of Instagram images to predict hashtags, then fine-tuned.
— Yann LeCun (@ylecun) February 12, 2019
