1 code implementation • 15 Jun 2022 • Minkyu Choi, Yizhen Zhang, Kuan Han, Xiaokai Wang, Zhongming Liu
Compared to human vision, computer vision based on convolutional neural networks (CNN) are more vulnerable to adversarial noises.
no code implementations • NeurIPS 2021 • Yizhen Zhang, Minkyu Choi, Kuan Han, Zhongming Liu
After training, the language stream of this model is a stand-alone language model capable of embedding concepts in a visually grounded semantic space.
no code implementations • 7 Mar 2018 • Minkyu Choi, Takazumi Matsumoto, Minju Jung, Jun Tani
The current paper presents how a predictive coding type deep recurrent neural networks can generate vision-based goal-directed plans based on prior learning experience by examining experiment results using a real arm robot.
no code implementations • 2 Aug 2017 • Minkyu Choi, Jun Tani
The paper examines how model performance during pattern generation as well as predictive imitation varies depending on the stage of learning.
no code implementations • 8 Jun 2017 • Jungsik Hwang, Jinhyung Kim, Ahmadreza Ahmadi, Minkyu Choi, Jun Tani
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns.
no code implementations • 6 Jun 2016 • Minkyu Choi, Jun Tani
The current paper presents a novel recurrent neural network model, the predictive multiple spatio-temporal scales RNN (P-MSTRNN), which can generate as well as recognize dynamic visual patterns in the predictive coding framework.
no code implementations • 9 Jul 2015 • Jungsik Hwang, Minju Jung, Naveen Madapana, Jinhyung Kim, Minkyu Choi, Jun Tani
The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).