Hydra Attention: Efficient Attention with Many Heads
نویسندگان
چکیده
While transformers have begun to dominate many tasks in vision, applying them large images is still computationally difficult. A reason for this that self-attention scales quadratically with the number of tokens, which turn, image size. On larger (e.g., 1080p), over 60% total computation network spent solely on creating and attention matrices. We take a step toward solving issue by introducing Hydra Attention, an extremely efficient operation Vision Transformers (ViTs). Paradoxically, efficiency comes from taking multi-head its extreme: using as heads there are features, Attention linear both tokens features no hidden constants, making it significantly faster than standard off-the-shelf ViT-B/16 factor token count. Moreover, retains high accuracy ImageNet and, some cases, actually improves it.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2023
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-25082-8_3