Multi-Layer Perceptrons Using Complex Token Mixing
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Solution Overview
Problem
Existing multi-layer perceptron (MLP) architectures in computer vision face challenges due to high computational costs and inferior performance compared to state-of-the-art Transformer and CNN architectures, primarily because they use fixed weights for token aggregation, neglecting semantic differences in input data.
Innovation Solution
The method involves re-parameterizing tokens into complex forms with amplitudes and phases, allowing for dynamic weight modulation through superposition, enhancing the MLP architecture's ability to adapt to varying inputs and improve performance by incorporating phase-aware token mixing and channel-mixing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MLP architectures use fixed weights for token aggregation, then computational cost is reduced, but performance deteriorates due to inability to capture semantic differences
Solution Approach 1:
The patent applies dynamics by transforming static fixed weights into dynamic adaptive weights. Each token is represented as a complex number with amplitude and phase, where the phase encodes semantic information. The weight matrix is transformed into a complex-valued matrix that dynamically adjusts based on token phases, enabling the model to adapt to different input semantics without increasing computational architecture complexity
Solution Approach 2:
The patent changes parameters by representing tokens as complex numbers instead of real numbers, introducing phase and amplitude dimensions. This parameter transformation allows the model to capture semantic differences through phase variations while maintaining the same computational operations, effectively resolving the contradiction between performance and computational cost
2Adaptability or versatility
If MLP architectures process tokens with fixed weights, then device complexity is reduced, but adaptability to different input semantic information deteriorates
Solution Approach 1:
The patent makes the weight matrix dynamic by incorporating token phase information into the weighting process. The complex-valued weight matrix adapts to different input tokens based on their phases, enabling the model to handle diverse semantic information dynamically without requiring separate models or increased architectural complexity
Solution Approach 2:
The model achieves self-service by automatically adapting weights based on the phase information inherent in the input tokens themselves. The complex-valued weight matrix self-adjusts according to the semantic content of the tokens, eliminating the need for external adaptation mechanisms or pre-computed semantic representations
Data Source
AI summary
A method is provided for data processing based on a multi-layer perceptrons (MLP) architecture. The method comprises determining a plurality of tokens for a piece of data, generating an amplitude and a phase for each of the plurality of tokens, optimizing the plurality of tokens by mixing the plurality of tokens based on the amplitudes and the phases, and determining one or more features included in the piece of data based on the plurality of optimized tokens. Each token includes information associated with a segment of the piece of data.