Maki, Atsuto

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

Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks

Imbalanced Data Classification TechniquesAdvanced Neural Network Applications

Cited by 3,088

Factors of Transferability for a Generic ConvNet Representation

Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A., & Carlsson, S. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 301

[Paper] Visual Instance Retrieval with Deep Convolutional Networks

Razavian, A. S., Sullivan, J., Carlsson, S., & Maki, A. (2016) · ITE Transactions on Media Technology and Applications

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 276

Deep predictive policy training using reinforcement learning

Ghadirzadeh, A., Maki, A., Kragic, D., & Bjorkman, M. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 113

Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector

Feng, D., Wei, X., Rosenbaum, L., Maki, A., & Dietmayer, K. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV)

Machine Learning and AlgorithmsAdvanced Neural Network Applications

Cited by 85

Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks

Rabadán, M. M. I., Pieropan, A., Azizpour, H., & Maki, A. (2022) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 0