Deep Forest: Towards an Alternative to Deep Neural Networks
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Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#2HN just won't be the same
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#3So if this works well why is there no comparison on ImageNet?
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#4I don't know about the others, but the two visions dataset they compare to (MNIST and the face recognition one) are small datasets and the CNN they compare to doesn't seem very state of the art.
It also seems each layer of random forest just concatenates a class distribution to the original feature vector. So this doesn't seem to get the same "hierarchy of features" benefit that you get in large-scale CNN and DNN.
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#5So if this works well why is there no comparison on ImageNet?
That's exactly what I was going to post, but you beat me.
I found surprising that they claim competitive performance over neural networks just using a CPU. The advantage I think neural networks have over all other methods is how efficiently they run on GPUs. I would love to see other methods take advantage of modern hardware or parallelism and see if they can catch up with neural networks on image classification.
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#6I don't know about the others, but the two visions dataset they compare to (MNIST and the face recognition one) are small datasets and the CNN they compare to doesn't seem very state of the art. It also seems each layer of random forest just concatenates a class distribution to the original feature vector. So this doesn't seem to get the same "hierarchy of features" benefit that you get in large-scale CNN and DNN.
^ The authors time and effort they observe it takes to create state-of-the-art CNNs, but their point-of-comparison CNNs look to be fairly simple -- I don't see an AlexNet or something for some of these tasks either just as a point of comparison even if not a fully relevant one
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#7I don't know about the others, but the two visions dataset they compare to (MNIST and the face recognition one) are small datasets and the CNN they compare to doesn't seem very state of the art. It also seems each layer of random forest just concatenates a class distribution to the original feature vector. So this doesn't seem to get the same "hierarchy of features" benefit that you get in large-scale CNN and DNN.
To your point that they are comparing small datasets. I dont see that as a problem. If they achieve better results on small datasets that is a great achievement, as often the bottleneck is the size of the dataset rather than computation time.
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#8[deleted]
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#9So if this works well why is there no comparison on ImageNet?
In the conclusion section the authors said that :
"If we had stronger computational facilities, we would like to try big data and deeper forest, which is left for future work." and that:
"As a seminar study, we have only explored a little in this direction."
Re: Deep Forest: Towards an Alternative to Deep Neural Networks
#10I don't know about the others, but the two visions dataset they compare to (MNIST and the face recognition one) are small datasets and the CNN they compare to doesn't seem very state of the art. It also seems each layer of random forest just concatenates a class distribution to the original feature vector. So this doesn't seem to get the same "hierarchy of features" benefit that you get in large-scale CNN and DNN.
Comparable to SOTA circa 1998.