MetaMixer Is All You Need

• URL: http://arxiv.org/abs/2406.02021v1
• Date: Tue, 4 Jun 2024 07:00:14 GMT
• Title: MetaMixer Is All You Need
• Authors: Seokju Yun, Dongheon Lee, Youngmin Ro,
• Abstract summary: Transformer, composed of self-attention and Feed-Forward Network, has revolutionized the landscape of network design across various vision tasks. Recent works also show that FFN functions like key-value memories. We propose converting self-attention into a more FFN-like efficient token mixer with only convolutions.
• Score: 6.8410780175245165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer, composed of self-attention and Feed-Forward Network, has revolutionized the landscape of network design across various vision tasks. FFN is a versatile operator seamlessly integrated into nearly all AI models to effectively harness rich representations. Recent works also show that FFN functions like key-value memories. Thus, akin to the query-key-value mechanism within self-attention, FFN can be viewed as a memory network, where the input serves as query and the two projection weights operate as keys and values, respectively. We hypothesize that the importance lies in query-key-value framework itself rather than in self-attention. To verify this, we propose converting self-attention into a more FFN-like efficient token mixer with only convolutions while retaining query-key-value framework, namely FFNification. Specifically, FFNification replaces query-key and attention coefficient-value interactions with large kernel convolutions and adopts GELU activation function instead of softmax. The derived token mixer, FFNified attention, serves as key-value memories for detecting locally distributed spatial patterns, and operates in the opposite dimension to the ConvNeXt block within each corresponding sub-operation of the query-key-value framework. Building upon the above two modules, we present a family of Fast-Forward Networks. Our FFNet achieves remarkable performance improvements over previous state-of-the-art methods across a wide range of tasks. The strong and general performance of our proposed method validates our hypothesis and leads us to introduce MetaMixer, a general mixer architecture that does not specify sub-operations within the query-key-value framework. We show that using only simple operations like convolution and GELU in the MetaMixer can achieve superior performance.

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