Swin Transformer shows resilience to FP4 quantization in anomaly segmentation

A new research paper explores how model architecture, scale, and specific quantization-aware training (QAT) recipes affect the quality of anomaly segmentation models when using FP4 precision. The study found that attention-based architectures, such as the Swin Transformer, are significantly more resilient to different QAT recipe choices compared to CNNs, especially at larger scales. The findings suggest that Swin Transformers are a recommended choice for FP4-quantized anomaly segmentation tasks due to their robustness. AI

IMPACT Highlights the importance of architecture choice for quantization robustness, potentially guiding future model development for efficient inference.

RANK_REASON This is a research paper detailing findings on model architecture and quantization techniques for anomaly segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• CNN
• Swin Transformer

• paper
• model release

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