Titlebook: Continual Semi-Supervised Learning; First International Fabio Cuzzolin,Kevin Cannons,Vincenzo Lomonaco Conference proceedings 2022 The Edi

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,Transfer and Continual Supervised Learning for Robotic Grasping Through Grasping Features,se of Grasping Features. Given a network architecture composed by a CNN (Convolutional Neural Network) followed by a FCC (Fully Connected Cascade Neural Network), we exploit high-level features specific of the grasping tasks, as extracted by the convolutional network from RGBD images. These features

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,Unsupervised Continual Learning via Self-adaptive Deep Clustering Approach,or unlimited access of ground truth incurring expensive labelling cost. Another issue lies in the problem of task boundaries and task IDs which must be known for model’s updates or model’s predictions hindering feasibility for real-time deployment. Knowledge Retention in Self-Adaptive Deep Continual

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,Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments,ng process in machines is a challenging task, also due to the inherent difficulty in creating conditions for designing continuously evolving dynamics that are typical of the real-world. Many existing research works usually involve training and testing of virtual agents on datasets of static images o

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Conference proceedings 2022.This book constitutes the proceedings of the First International Workshop on Continual Semi-Supervised Learning, CSSL 2021, which took place as a virtual event during August 2021.The 9 full papers and 0 short papers included in this book were carefully reviewed and selected from 14 submissions..

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