6 citations · 9 across the 3 of their papers we have counts for
5 papers
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics
N. Passalis, S. Pedrazzi, R. Babuska +15
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their r…
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation
Maria Tzelepi, Anastasios Tefas
Knowledge Distillation has been established as a highly promising approach for training compact and faster models by transferring knowledge from heavyweight and powerful models. Ho…
Quadratic mutual information regularization in real-time deep CNN models
Maria Tzelepi, Anastasios Tefas
In this paper, regularized lightweight deep convolutional neural network models, capable of effectively operating in real-time on devices with restricted computational power for hi…
Semantic Scene Segmentation for Robotics Applications
Maria Tzelepi, Anastasios Tefas
Semantic scene segmentation plays a critical role in a wide range of robotics applications, e.g., autonomous navigation. These applications are accompanied by specific computationa…
Heterogeneous Knowledge Distillation using Information Flow Modeling
Nikolaos Passalis, Maria Tzelepi, Anastasios Tefas
Knowledge Distillation (KD) methods are capable of transferring the knowledge encoded in a large and complex teacher into a smaller and faster student. Early methods were usually l…