2nd International Workshop on Practical
Deep Learning in the Wild
Workshop at AAAI Conference on Artificial Intelligence 2023
Deep learning has achieved great success for artificial intelligence (AI) in many advanced tasks, such as computer vision,
natural language processing, and robotics. However, research in the AI field also shows that their performance in the wild
is far from practical towards open-world data and scenarios. Besides the accuracy that is widely concerned in deep learning,
the phenomena are significantly related to the studies about model efficiency and robustness, which we abstract as Practical
Deep Learning in the Wild (Practical-DL). Regarding model efficiency, in contrast to the ideal environment, it is impractical
to train a huge neural network containing billions of parameters using a large-scale high-quality dataset and then deploy it
to an edge device in practice. Meanwhile, considering model robustness, input data with noises frequently occur in open-world
scenarios, which presents critical challenges for the building of robust AI systems in practice. Moreover, existing research
presents that there is a trade-off between the robustness and accuracy of deep learning models, while in the context of efficient
deep learning with limited resources, it is more challenging to achieve a better trade-off under the premise of satisfying
efficiency. These complex demands would bring profound implications and an explosion of interest for research into the topic
of this Practical-DL workshop in AAAI 2023, namely building practical AI with efficient and robust deep learning models.
Important: The submission deadline has been extended to 15th Nov 2022 (AoE).