Call for Papers

Recently, the success of deep learning in AI has attracted great attention from academia and industry. However, research shows that the performance of models in the wild is far from practical due to the lack of efficiency and robustness towards open-world data and scenarios. We welcome research contributions related to the following (but not limited to) topics:
  • Network quantization and binarization
  • Adversarial attacking deep learning systems
  • Neural architecture search (NAS)
  • Robust architectures against adversarial attacks
  • Hardware implementation and on-device deployment
  • Benchmark for evaluating model robustness
  • New methodologies and architectures for efficient and rubost deep learning
Submission Format: Submissions papers (.pdf format) must use the AAAI Article Template and be anonymized and follow AAAI 2023 author instructions. The workshop considers two types of submissions: (1) Long Paper: Papers are limited to 7 pages excluding references; (2) Extended Abstract: Papers are limited to 4 pages including references.
The excellent papers will be invited to the Special Issue of Pattern Recognition journal for publication consideration.
Submission Site: https://cmt3.research.microsoft.com/PracticalDL2023/
Submission Due: 15th Nov, 2022 (AoE)

Practical AI Challenge

The challenge is held jointly with the "2nd International Workshop on Practical Deep Learning in the Wild" at AAAI 2023.
  • Evaluating and exploring the challenge of building practical deep-learning models;
  • Encouraging technological innovation for efficient and robust AI algorithms;
  • Emphasizing the size, latency, power, accuracy, safety, and generalization ability of the neural network.
Track I: Efficient and Robust network for specific hardware
  • Recently, there are many efficient networks for mobile vision applications or edge devices. However, most of them are only concerned with the theoretical speedup of the model(such as FLOPs, memory), ignoring the speedup of the networks on the practical hardware devices. There are still many problems preventing the network achieving theoretical speedup and accuracy improvement in practical hardware.
  • To accelerate the development of practical AI, we organize this challenge track for motivating novel easier-to-deploy networks with high accuracy performance. Participants are encouraged to select a specific hardware platform, such as Atlas300/RV1126 for smart city or camera, GPU T4 for cloud computing, mobile GPU/DSP for mobile phones for and TDA4VM for autonomous driving, to develop efficient and robust networks according to their hardware characteristics.
Track II: Efficient and Robust network across multiple hardware.
  • Most hardware-aware networks aim to design an efficient network for one specific hardware platform. However, due to the different characteristics among different hardware, it is hard to achieve the same speedup on all hardware platforms. There are still many challenges finding an efficient and robust network which can achieve both speed and accuracy performance across different hardware platforms.
  • To accelerate the research on building efficient and robust networks across different hardware, we organize this challenge track. Participants are encouraged to design an efficient network which can be deployed on all hardware platforms we provide.
Challenge Site: https://practical-dl.sensecore.cn
Submission Due: 31th Dec, 2022 (AoE)