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Year-End Seminar 2018

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Material of year-end seminar of Social Intelligence Research Team of artificial intelligence research center of National Institute of Advanced Industrial Science and Technology.

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Year-End Seminar 2018

  1. 1. Senior Researcher Masayuki Tanaka 2018/12/27 Social Intelligence Research Team Year-End Seminar http://www.ok.sc.e.titech.ac.jp/~mtanaka https://twitter.com/likesilkto
  2. 2. Recent Conference Presentations Gradient-Based Low-Light Image Enhancement Masayuki Tanaka, Takashi Shibata and Masatoshi Okutomi ProceedingsofIEEEInternationalConferenceonConsumerElectronics(ICCE2019),January,2019 Pixelwise JPEG compression detection and quality factor estimation based on convolutional neural network Kazutaka Uchida, Masayuki Tanaka, and Masatoshi Okutomi Proceedings of IS&T International Symposium on Electronic Imaging (EI2019), January, 2019 Disparity Map Estimation from Cross-Modal Stereo Thapanapong Rukkanchanunt, Takashi Shibata, Masayuki Tanaka and Masatoshi Okutomi Proceedings of 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP2018), pp.988-992, November, 2018 Non-blindImageRestorationBasedonConvolutional NeuralNetwork Kazutaka Uchida, Masayuki Tanaka and Masatoshi Okutomi ProceedingsofIEEE7thGlobalConferenceonConsumerElectronics(GCCE2018),pp.12-16,October,2018 RemoteHeartRateMeasurementfromRGB-NIRVideoBasedonSpatial andSpectralFacePatchSelection ShiikaKado,YusukeMonno,KentaMoriwaki,KazunoriYoshizaki,MasayukiTanakaandMasatoshiOkutomi Proceedings of 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2018), pp.5676-5680, July, 2018
  3. 3. Activation Functions for DNNs Input x Activation function Weight Output y Conv. Activation function Input x Output y Activation functions Sigmoid tanh ReLU 𝜎𝜎 𝑥𝑥 = 1 1 + 𝑒𝑒−𝑥𝑥 max(𝑥𝑥, 0)
  4. 4. Advanced Activation Functions ReLU max(𝑥𝑥, 0) � 𝑥𝑥 (𝑥𝑥 ≥ 0) 𝛼𝛼 (𝑥𝑥 < 0) Leaky ReLU Parametric ReLU swish, SiL 𝑥𝑥 𝜎𝜎 𝑤𝑤𝑤𝑤 + 𝑏𝑏 Existing activation functions are element-wise function. Dying ReLU: Dead ReLU units always return zero.
  5. 5. WiG: Weighted Sigmoid Gate (Proposed) Existing activation functions are element-wise function. Sigmoid Gated Network can be used as activation function. Weight Activation function Weight Activation networkunit Proposed WiG (Weighted sigmoid gate unit) W × Wg WiG activation unit It is compatible to existing activation functions. It includes the ReLU. Sigmoid W Wg × My recommendation is: You can improve the network performance just by replacing the ReLU by the proposed WiG.
  6. 6. Experimental Validations Object recognition Average accuracy Image denoising The reproduction code is available http://www.ok.sc.e.titech.ac.jp/~mtanaka/proj/WiG/
  7. 7. Reference Masayuki Tanaka, Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network, arXiv preprint arXiv:1810.01829, 2018. http://www.ok.sc.e.titech.ac.jp/~mtanaka/proj/WiG/ V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10), 2010, pp. 807–814. P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for activation functions,” arXiv preprint arXiv:1710.05941, 2017. S. Elfwing, E. Uchibe, and K. Doya, “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,”arXiv preprint arXiv:1702.03118, 2017. A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in International Conference on Machine Learning (ICML), vol. 30, no. 1, 2013, p. 3. K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1026–1034.
  8. 8. Promotion: Train1000 project The deep learning usually requires a huge size of training data to improve the performance. However, a training with such a huge data needs high computational cost in terms of both computational power and time. It is very tough especially for beginners. In practice, it is hard to collect a huge number of annotated training samples. I think that 1,000 samples are minimum number for the training of the network. The training with 1,000 samples also includes technical challenges. One of them is to improve generalization performance while avoiding the over fitting. Let’s enjoy train with 1000. http://www.ok.sc.e.titech.ac.jp/~mtanaka/proj/train1000/ Sample codes of matlab and keras for mnits and cifar are available.

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