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Found 7 papers, 1 papers with code
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises
Compared to human vision, computer vision based on convolutional neural networks (CNN) are more vulnerable to adversarial noises.
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning
After training, the language stream of this model is a stand-alone language model capable of embedding concepts in a visually grounded semantic space.
Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model
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.
Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model
The paper examines how model performance during pattern generation as well as predictive imitation varies depending on the stage of learning.
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns.
Predictive Coding for Dynamic Vision : Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model
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.
Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination
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).
