

#044 - Data-efficient Image Transformers (Hugo Touvron)
Feb 25, 2021
Hugo Touvron, a PhD student at Facebook AI Research and the primary author of the Data-efficient Image Transformers paper, shares insights on revolutionizing vision models. He explains how novel training strategies and a unique distillation token dramatically improve sample efficiency. The conversation dives into the balance of data augmentation, the implications of transformers compared to CNNs, and challenges in achieving data-driven models. Hugo also reflects on his experiences in a corporate PhD program and the future prospects of transformers in computer vision.
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Transformers' Flexibility
- Transformers offer more flexibility than ConvNets due to the absence of hard-coded convolutions.
- This makes them more adaptable and potentially more powerful.
Patch Extraction as Convolution
- While transformers don't use traditional convolutions, the patch extraction process can be seen as a type of convolution.
- It applies the same transformation to every patch initially.
Location-Based Patch Processing
- Consider treating different image patches differently based on their location.
- This might be beneficial in scenarios like self-driving cars where certain areas are more important.