论文标题
具有动态矢量量化的自适应离散通信瓶颈
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
论文作者
论文摘要
矢量量化（VQ）是离散潜在表示的方法，并已成为深度学习工具包的主要组成部分。从理论上讲，从经验上表明，代表性的离散化会导致改进的概括，包括在加强学习中可以使用离散化来瓶颈多代理通信，以促进代理商的专业化和鲁棒性。大多数基于VQ的方法的离散化紧密度由表示向量和代码簿大小的离散代码数量定义，这些代码被固定为超参数。在这项工作中，我们建议学习，以动态选择以输入为条件的离散化紧密度，这是基于以下假设：数据自然包含需要不同级别的代表性粗糙度的复杂性变化。我们表明，交流瓶颈的动态紧密度可以改善视觉推理和强化学习任务的模型性能。
Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically shown that discretization of representations leads to improved generalization, including in reinforcement learning where discretization can be used to bottleneck multi-agent communication to promote agent specialization and robustness. The discretization tightness of most VQ-based methods is defined by the number of discrete codes in the representation vector and the codebook size, which are fixed as hyperparameters. In this work, we propose learning to dynamically select discretization tightness conditioned on inputs, based on the hypothesis that data naturally contains variations in complexity that call for different levels of representational coarseness. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
