Microsoft wins ImageNet 2015 through feedforward LSTM without gates

Microsoft Wins ImageNet 2015 through
Highway Net (or Feedforward LSTM) without Gates

Jürgen Schmidhuber

Microsoft Research dominated the ImageNet 2015 contest with a very deep neural network of 150 layers [1]. Congrats to Kaiming He & Xiangyu Zhang & Shaoqing Ren & Jian Sun on the great results [2]!

Their Residual Net or ResNet [1] of December 2015 is a special case of our Highway Networks [4] of May 2015, the first very deep feedforward networks with hundreds of layers. Highway nets are essentially feedforward versions of recurrent Long Short-Term Memory (LSTM) networks [3] with forget gates (or gated recurrent units) [5].

Let g, t, h denote non-linear differentiable functions. Each non-input layer of a Highway Net computes g(x)x + t(x)h(x), where x is the data from the previous layer. (Like LSTM [3] with forget gates [5] for recurrent networks.)

The CNN layers of ResNets [1] do the same with g(x)=1 (a typical Highway Net initialisation) and t(x)=1, essentially like a Highway Net or a feedforward LSTM [3] without gates.

This is the basic ingredient required to overcome the fundamental deep learning problem of vanishing or exploding gradients. The authors mention it [1], but do not mention my very first student Sepp Hochreiter (now professor) who identified and analyzed it in 1991, years before anybody else did [6].

Apart from the quibbles above, I liked the paper [1] a lot. LSTM concepts keep invading CNN territory [e.g., 7a-e], also through GPU-friendly multi-dimensional LSTMs [8].


References

[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Deep Residual Learning for Image Recognition. TR arxiv:1512.03385, Dec 2015.

[2] ImageNet Large Scale Visual Recognition Challenge 2015 (ILSVRC2015): Results

[3] S. Hochreiter, J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735-1780, 1997. Based on TR FKI-207-95, TUM (1995). PDF. Led to a lot of follow-up work, and is now heavily used by leading IT companies all over the world.

[4] R. K. Srivastava, K. Greff, J. Schmidhuber. Highway networks. TR arxiv:1505.00387 (May 2015) and arXiv:1507.06228 (July 2015). Also at NIPS'2015.

[5] F. A. Gers, J. Schmidhuber, F. Cummins. Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10):2451-2471, 2000. PDF.

[6] S. Hochreiter. Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, TU Munich, 1991. Advisor: J. Schmidhuber. Overview.

[7a] 2011: First superhuman CNNs
[7b] 2011: First human-competitive CNNs for handwriting
[7c] 2012: First CNN to win segmentation contest
[7d] 2012: First CNN to win contest on object discovery in large images
[7e] Deep Learning. Scholarpedia, 10(11):32832, 2015

[8] M. Stollenga, W. Byeon, M. Liwicki, J. Schmidhuber. Parallel Multi-Dimensional LSTM, with Application to Fast Biomedical Volumetric Image Segmentation. NIPS 2015; arxiv:1506.07452.


Can you spot the Fibonacci pattern in the graphics above?
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