Clad: A realistic continual learning benchmark for autonomous driving
In this paper we describe the design and the ideas motivating a new Continual Learning
benchmark for Autonomous Driving (CLAD), that focuses on the problems of object …
benchmark for Autonomous Driving (CLAD), that focuses on the problems of object …
Knowledge distillation in vision transformers: A critical review
In Natural Language Processing (NLP), Transformers have already revolutionized the field
by utilizing an attention-based encoder-decoder model. Recently, some pioneering works …
by utilizing an attention-based encoder-decoder model. Recently, some pioneering works …
Continually learning self-supervised representations with projected functional regularization
Recent self-supervised learning methods are able to learn high-quality image
representations and are closing the gap with supervised approaches. However, these …
representations and are closing the gap with supervised approaches. However, these …
Birt: Bio-inspired replay in vision transformers for continual learning
The ability of deep neural networks to continually learn and adapt to a sequence of tasks
has remained challenging due to catastrophic forgetting of previously learned tasks …
has remained challenging due to catastrophic forgetting of previously learned tasks …
Continual named entity recognition without catastrophic forgetting
Continual Named Entity Recognition (CNER) is a burgeoning area, which involves updating
an existing model by incorporating new entity types sequentially. Nevertheless, continual …
an existing model by incorporating new entity types sequentially. Nevertheless, continual …
Continual multimodal knowledge graph construction
Current Multimodal Knowledge Graph Construction (MKGC) models struggle with the real-
world dynamism of continuously emerging entities and relations, often succumbing to …
world dynamism of continuously emerging entities and relations, often succumbing to …
Digital twin robotic system with continuous learning for grasp detection in variable scenes
With the emergence of digitalization technology, digital twin bridges the gap between
physical and virtual worlds in industrial production with synchronization, reliability, and …
physical and virtual worlds in industrial production with synchronization, reliability, and …
Simpler is better: off-the-shelf continual learning through pretrained backbones
F Pelosin - arXiv preprint arXiv:2205.01586, 2022 - arxiv.org
In this short paper, we propose a baseline (off-the-shelf) for Continual Learning of Computer
Vision problems, by leveraging the power of pretrained models. By doing so, we devise a …
Vision problems, by leveraging the power of pretrained models. By doing so, we devise a …
Task-attentive transformer architecture for continual learning of vision-and-language tasks using knowledge distillation
The size and the computational load of fine-tuning large-scale pre-trained neural network
are becoming two major obstacles in adopting machine learning in many applications …
are becoming two major obstacles in adopting machine learning in many applications …
Lifelong language learning with adaptive uncertainty regularization
It has been a long-standing goal in natural language processing (NLP) to learn a general
linguistic intelligence model that can perform well on many different NLP tasks continually …
linguistic intelligence model that can perform well on many different NLP tasks continually …