WeiT applies Kronecker-based constraints during pre-training to disentangle size-agnostic knowledge into reusable weight templates and size-specific lightweight scalers for efficient initialization of models with varying depths and widths.
Specifically, for each stage s, the depthwise convolution weight is denoted as W dw s ∈R Cs×1×k×k
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Constraint-based Pre-training: From Structured Constraints to Scalable Model Initialization
WeiT applies Kronecker-based constraints during pre-training to disentangle size-agnostic knowledge into reusable weight templates and size-specific lightweight scalers for efficient initialization of models with varying depths and widths.