In this work we address flexibility in deep learning by means of transductive
reasoning. For adaptation to new tasks or new data, existing methods typically
involve tuning of learnable parameters or even complete re-training from
scratch, rendering such approaches unflexible in practice. We argue that the
notion of separating computation from memory by the means of transduction can
act as a stepping stone for solving these issues. We therefore propose PARMESAN
(parameter-free memory search and transduction), a scalable transduction method
which leverages a memory module for solving dense prediction tasks. At
inference, hidden representations in memory are being searched to find
corresponding examples. In contrast to other methods, PARMESAN learns without
the requirement for any continuous training or fine-tuning of learnable
parameters simply by modifying the memory content. Our method is compatible
with commonly used neural architectures and canonically transfers to 1D, 2D,
and 3D grid-based data. We demonstrate the capabilities of our approach at
complex tasks such as continual and few-shot learning. PARMESAN learns up to
370 times faster than common baselines while being on par in terms of
predictive performance, knowledge retention, and data-efficiency.

通过转导推理的方式解决深度学习中的灵活性问题，提出了 PARMESAN（无参数记忆搜索和转导），一种可扩展的转导方法，通过修改记忆内容实现无需连续训练或微调可学参数的学习，与常用的神经架构兼容，并在连续学习和少样本学习等任务中展现了较快的学习速度和高预测性能。