Deep convolutional networks have recently achieved great success in video
recognition, yet their practical realization remains a challenge due to the
large amount of computational resources required to achieve robust recognition.
Motivated by the effectiveness of quantization for boosting efficiency, in this
paper, we propose a dynamic network quantization framework, that selects
optimal precision for each frame conditioned on the input for efficient video
recognition. Specifically, given a video clip, we train a very lightweight
network in parallel with the recognition network, to produce a dynamic policy
indicating which numerical precision to be used per frame in recognizing
videos. We train both networks effectively using standard backpropagation with
a loss to achieve both competitive performance and resource efficiency required
for video recognition. Extensive experiments on four challenging diverse
benchmark datasets demonstrate that our proposed approach provides significant
savings in computation and memory usage while outperforming the existing
state-of-the-art methods.

本文提出动态网络量化框架，依据每个输入帧选择最佳精度以实现高效的视频识别，实验结果显示，在四种具有挑战性的基准数据集上，与现有先进方法相比，该方法提供了显著的计算和内存使用节约，同时保持竞争性能。

动态网络量化用于高效视频推理

Dynamic Network Quantization for Efficient Video Inference

We present any-precision deep neural networks (DNNs), which are trained with
a new method that allows the learned DNNs to be flexible in numerical precision
during inference. The same model in runtime can be flexibly and directly set to
different bit-widths, by truncating the least significant bits, to support
dynamic speed and accuracy trade-off. When all layers are set to low-bits, we
show that the model achieved accuracy comparable to dedicated models trained at
the same precision. This nice property facilitates flexible deployment of deep
learning models in real-world applications, where in practice trade-offs
between model accuracy and runtime efficiency are often sought. Previous
literature presents solutions to train models at each individual fixed
efficiency/accuracy trade-off point. But how to produce a model flexible in
runtime precision is largely unexplored. When the demand of efficiency/accuracy
trade-off varies from time to time or even dynamically changes in runtime, it
is infeasible to re-train models accordingly, and the storage budget may forbid
keeping multiple models. Our proposed framework achieves this flexibility
without performance degradation. More importantly, we demonstrate that this
achievement is agnostic to model architectures and applicable to multiple
vision tasks. Our code is released at
this https URL

提出了一种新方法训练任意精度深度神经网络（DNNs），可以在推断时灵活地设置数字精度，从而支持动态速度和准确度的折衷，并且该方法不会降低性能，该方法适用于多个视觉任务和模型结构

任意精度深度神经网络

Any-Precision Deep Neural Networks

It is known that training deep neural networks, in particular, deep
convolutional networks, with aggressively reduced numerical precision is
challenging. The stochastic gradient descent algorithm becomes unstable in the
presence of noisy gradient updates resulting from arithmetic with limited
numeric precision. One of the well-accepted solutions facilitating the training
of low precision fixed point networks is stochastic rounding. However, to the
best of our knowledge, the source of the instability in training neural
networks with noisy gradient updates has not been well investigated. This work
is an attempt to draw a theoretical connection between low numerical precision
and training algorithm stability. In doing so, we will also propose and verify
through experiments methods that are able to improve the training performance
of deep convolutional networks in fixed point.

低数字精度如何影响神经网络的稳定性，以及如何通过随机舍入等方法提高固定点深度卷积网络的训练性能。