Automated optical inspection is crucial for maintaining quality in modern manufacturing. While deep-learning-based anomaly detection (AD) has shown promise, its deployment is often hindered by “contextual variations,” namely benign environmental changes such as lighting or camera noise that are irrelevant to product de...
Kota Dohi, Naoya Takeishi, T. Yairi· IEEE Access· 0 citations
Context-aware dynamic trees allocate the speculative decoding budget according to draft path probabilities, adapting their depth and branching to the current context. Under stochastic decoding, however, we find that this structural advantage does not always compensate for the acceptance gains of random sampling paired...
Simulation results for spacecraft landing demonstrate that the proposed method achieves near-optimal performance while maintaining provable stability throughout the descent.
This study proposes a simple yet effective physics-informed approach based on diffusion guidance with self-generated data augmentation that significantly reduces the deviations compared with standard diffusion models but also achieves further reductions when combined with existing physics-constrained diffusion methods.
Akira Osaka, Naoya Takeishi, T. Yairi· 0 citations
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