Search Results for author: Atsuto Maki

Found 11 papers, 1 papers with code

Discriminant analysis based on projection onto generalized difference subspace

no code implementations29 Oct 2019 Kazuhiro Fukui, Naoya Sogi, Takumi Kobayashi, Jing-Hao Xue, Atsuto Maki

To avoid the difficulty, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion.

Regularizing CNN Transfer Learning with Randomised Regression

no code implementations CVPR 2020 Yang Zhong, Atsuto Maki

That is, a CNN is efficiently regularized without additional resources of data or prior domain expertise.

Transfer Learning

Target Aware Network Adaptation for Efficient Representation Learning

no code implementations2 Oct 2018 Yang Zhong, Vladimir Li, Ryuzo Okada, Atsuto Maki

This paper presents an automatic network adaptation method that finds a ConvNet structure well-suited to a given target task, e. g., image classification, for efficiency as well as accuracy in transfer learning.

Image Classification Representation Learning +1

A multitask deep learning model for real-time deployment in embedded systems

no code implementations31 Oct 2017 Miquel Martí, Atsuto Maki

We propose an approach to Multitask Learning (MTL) to make deep learning models faster and lighter for applications in which multiple tasks need to be solved simultaneously, which is particularly useful in embedded, real-time systems.

Object Detection Semantic Segmentation

A systematic study of the class imbalance problem in convolutional neural networks

3 code implementations15 Oct 2017 Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski

In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities.

General Classification

A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction

no code implementations27 Jul 2016 Ali Ghadirzadeh, Judith Bütepage, Atsuto Maki, Danica Kragic, Mårten Björkman

Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior.

Gaussian Processes reinforcement-learning

Visual Instance Retrieval with Deep Convolutional Networks

no code implementations20 Dec 2014 Ali Sharif Razavian, Josephine Sullivan, Stefan Carlsson, Atsuto Maki

This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval.

Image Retrieval

Persistent Evidence of Local Image Properties in Generic ConvNets

no code implementations24 Nov 2014 Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, Stefan Carlsson

Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.

General Classification

Factors of Transferability for a Generic ConvNet Representation

no code implementations22 Jun 2014 Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson

In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target).

Dimensionality Reduction Representation Learning

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