Hyperdimensional Computing (HDC) is a brain-inspired and light-weight machine
learning method. It has received significant attention in the literature as a
candidate to be applied in the wearable internet of things, near-sensor
artificial intelligence applications and on-device processing. HDC is
computationally less complex than traditional deep learning algorithms and
typically achieves moderate to good classification performance. A key aspect
that determines the performance of HDC is the encoding of the input data to the
hyperdimensional (HD) space. This article proposes a novel light-weight
approach relying only on native HD arithmetic vector operations to encode
binarized images that preserves similarity of patterns at nearby locations by
using point of interest selection and local linear mapping. The method reaches
an accuracy of 97.35% on the test set for the MNIST data set and 84.12% for the
Fashion-MNIST data set. These results outperform other studies using baseline
HDC with different encoding approaches and are on par with more complex hybrid
HDC models. The proposed encoding approach also demonstrates a higher
robustness to noise and blur compared to the baseline encoding.

提出了一种基于超维计算的新型轻量级编码方法，通过兴趣点选择和局部线性映射来保留附近位置模式的相似性，达到了对 MNIST 和 Fashion-MNIST 数据集分别为 97.35% 和 84.12% 的准确率，并且相对于基准编码方法，该方法还表现出更高的噪音和模糊容忍度。

使用高维度计算的二进制图像编码框架

An Encoding Framework for Binarized Images using HyperDimensional  Computing

Distilling knowledge from a well-trained cumbersome network to a small one
has recently become a new research topic, as lightweight neural networks with
high performance are particularly in need in various resource-restricted
systems. This paper addresses the problem of distilling word embeddings for NLP
tasks. We propose an encoding approach to distill task-specific knowledge from
a set of high-dimensional embeddings, which can reduce model complexity by a
large margin as well as retain high accuracy, showing a good compromise between
efficiency and performance. Experiments in two tasks reveal the phenomenon that
distilling knowledge from cumbersome embeddings is better than directly
training neural networks with small embeddings.

本文提出一种编码方法，用于从高维词嵌入中提取特定任务的知识，旨在解决在各种资源受限系统中高性能的轻量级神经网络的需求问题。实验结果表明，从笨重的嵌入中提取知识优于使用小型嵌入直接训练神经网络，能保证高准确性的同时大幅减少模型复杂度。