arXiv:2303.18031v2 Announce Type: replace-cross Abstract: In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the data distribution differs between the training and inference phases. Domain generalization (DG) aims to handle the domain shift situation where the target domain of the inference phase is inaccessible during the model training. Open domain generalization (ODG) considers DG and OSR. Domain-augmented meta-learning (DAML) is a method targeting O
The continuous improvement of AI model robustness and generalization capabilities is an ongoing research frontier, driven by the need for more reliable real-world AI applications.
Improving domain generalization for AI models addressing both domain shift and unknown classes is critical for deploying AI in dynamic, unpredictable environments outside of controlled training data.
The focus on enhancing open domain generalization suggests a move towards AI systems that can adapt and perform robustly even when encountering novel data distributions or previously unseen classes.
- · AI developers
- · Robotics companies
- · Autonomous systems creators
- · Industries relying on AI for unknown recognition
- · AI models lacking robust generalization
- · Companies with highly specialized, non-adaptive AI
AI models will become more reliable and adaptable in real-world applications where data distribution shifts and unknown classes are common.
This improved adaptability could accelerate the deployment of autonomous systems in complex environments, such as self-driving cars or advanced robotics.
More robust and generalizable AI could broaden the scope of AI applications, potentially reducing the need for extensive retraining and human oversight in varied operational settings.
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Read at arXiv cs.AI