Training deep neural networks (DNNs) is time-consuming. While most existing
solutions try to overlap/schedule computation and communication for efficient
training, this paper goes one step further by skipping computing and
communication through DNN layer freezing. Our key insight is that the training
progress of internal DNN layers differs significantly, and front layers often
become well-trained much earlier than deep layers. To explore this, we first
introduce the notion of training plasticity to quantify the training progress
of internal DNN layers. Then we design Egeria, a knowledge-guided DNN training
system that employs semantic knowledge from a reference model to accurately
evaluate individual layers' training plasticity and safely freeze the converged
ones, saving their corresponding backward computation and communication. Our
reference model is generated on the fly using quantization techniques and runs
forward operations asynchronously on available CPUs to minimize the overhead.
In addition, Egeria caches the intermediate outputs of the frozen layers with
prefetching to further skip the forward computation. Our implementation and
testbed experiments with popular vision and language models show that Egeria
achieves 19%-43% training speedup w.r.t. the state-of-the-art without
sacrificing accuracy.

本文介绍了一种跳过计算和通信过程的深度神经网络培训方法，采用层冻结技术，通过量子化技术实时地生成参考模型，以达到评估每个层的训练效果并安全地冻结已经达到了期望的层的效果，从而获得 19% 至 43% 的培训速度提升。

Egeria: 基于知识引导的层冻结技术用于高效 DNN 训练

Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing

Image captioning, a popular topic in computer vision, has achieved
substantial progress in recent years. However, the distinctiveness of natural
descriptions is often overlooked in previous work. It is closely related to the
quality of captions, as distinctive captions are more likely to describe images
with their unique aspects. In this work, we propose a new learning method,
Contrastive Learning (CL), for image captioning. Specifically, via two
constraints formulated on top of a reference model, the proposed method can
encourage distinctiveness, while maintaining the overall quality of the
generated captions. We tested our method on two challenging datasets, where it
improves the baseline model by significant margins. We also showed in our
studies that the proposed method is generic and can be used for models with
various structures.

本文提出一种基于对比学习的图像字幕生成方法，即在参考模型的基础上，通过两个约束条件，鼓励生成独特的字幕，从而提高字幕的质量和独特性。我们在两个挑战性数据集上测试了该方法，并证明它是通用的并可用于不同结构的模型。