  Deep learning has witnessed significant advancements in recent years at the
cost of increasing training, inference, and model storage overhead. While
existing model compression methods strive to reduce the number of model
parameters while maintaining high accuracy, they inevitably necessitate the
re-training of the compressed model or impose architectural constraints. To
overcome these limitations, this paper presents a novel framework, termed
\textbf{K}nowledge \textbf{T}ranslation (KT), wherein a ``translation'' model
is trained to receive the parameters of a larger model and generate compressed
parameters. The concept of KT draws inspiration from language translation,
which effectively employs neural networks to convert different languages,
maintaining identical meaning. Accordingly, we explore the potential of neural
networks to convert models of disparate sizes, while preserving their
functionality. We propose a comprehensive framework for KT, introduce data
augmentation strategies to enhance model performance despite restricted
training data, and successfully demonstrate the feasibility of KT on the MNIST
dataset. Code is available at \url{https://github.com/zju-SWJ/KT}.
