The content delves into the issues surrounding Batch Normalization in video learning, particularly focusing on its implications for end-to-end training strategies in surgical workflow analysis. The study reveals how Batch Normalization affects model performance and proposes alternative approaches to mitigate these challenges.
Batch Normalization's unique properties pose obstacles for end-to-end learning, especially in tasks with sequential data like surgical workflow analysis. The study emphasizes the importance of understanding these pitfalls to enhance training strategies effectively.
By comparing different backbones and training methods, the research demonstrates that models without Batch Normalization outperform those with it, showcasing the significance of choosing appropriate normalization techniques for optimal performance.
Furthermore, freezing backbone layers can improve models' performance by increasing sequence lengths while maintaining batch diversity. This approach proves beneficial for both Batch Normalized and non-Batch Normalized models.
Overall, the study sheds light on the critical role of Batch Normalization in video learning tasks and provides insights into overcoming its limitations to achieve better results.
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by Dominik Rivo... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2203.07976.pdfDeeper Inquiries