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.

翻译：本文通过直推推理解决深度学习中的灵活性问题。针对新任务或新数据的适应，现有方法通常涉及可学习参数的调整甚至完全从头重新训练，导致此类方法在实践中缺乏灵活性。我们认为，通过直推手段将计算与记忆分离的理念可成为解决这些问题的基石。因此，我们提出PARMESAN（无参数记忆搜索与直推），这是一种利用记忆模块解决密集预测任务的可扩展直推方法。在推理阶段，系统通过搜索记忆中的隐藏表征来寻找对应示例。与其他方法不同，PARMESAN无需连续训练或微调可学习参数，仅通过修改记忆内容即可实现学习。该方法与常用神经网络架构兼容，并可规范地迁移至1D、2D和3D网格数据。我们通过在持续学习和少样本学习等复杂任务中展示了该方法的能力。与常见基线相比，PARMESAN的学习速度提升高达370倍，同时在预测性能、知识保留和数据效率方面保持同等水平。