Search Results for author: Chun-Nan Chou

Found 8 papers, 0 papers with code

G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data

no code implementations29 May 2019 Fu-Chieh Chang, Hao-Jen Wang, Chun-Nan Chou, Edward Y. Chang

Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications.

General Classification

MBS: Macroblock Scaling for CNN Model Reduction

no code implementations CVPR 2019 Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang

In this paper we propose the macroblock scaling (MBS) algorithm, which can be applied to various CNN architectures to reduce their model size.

BRIEF: Backward Reduction of CNNs with Information Flow Analysis

no code implementations16 Jul 2018 Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang

This paper proposes BRIEF, a backward reduction algorithm that explores compact CNN-model designs from the information flow perspective.

EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks

no code implementations19 Feb 2018 Sheng-Wei Chen, Chun-Nan Chou, Edward Y. Chang

For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate gradient (CG) based method.

Representation Learning on Large and Small Data

no code implementations25 Jul 2017 Chun-Nan Chou, Chuen-Kai Shie, Fu-Chieh Chang, Jocelyn Chang, Edward Y. Chang

Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation.

Melanoma Diagnosis Representation Learning +1

CLKN: Cascaded Lucas-Kanade Networks for Image Alignment

no code implementations CVPR 2017 Che-Han Chang, Chun-Nan Chou, Edward Y. Chang

The main component of this architecture is a Lucas-Kanade layer that performs the inverse compositional algorithm on convolutional feature maps.

Homography Estimation

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