Data Augmentation (DA) -- enriching training data by adding synthetic samples
-- is a technique widely adopted in Computer Vision (CV) and Natural Language
Processing (NLP) tasks to improve models performance. Yet, DA has struggled to
gain traction in networking contexts, particularly in Traffic Classification
(TC) tasks. In this work, we fulfill this gap by benchmarking 18 augmentation
functions applied to 3 TC datasets using packet time series as input
representation and considering a variety of training conditions. Our results
show that (i) DA can reap benefits previously unexplored with (ii)
augmentations acting on time series sequence order and masking being a better
suit for TC and (iii) simple latent space analysis can provide hints about why
augmentations have positive or negative effects.

数据增强（DA）在计算机视觉（CV）和自然语言处理（NLP）领域被广泛采用以提高模型性能，然而在网络环境中，尤其是在流量分类（TC）任务中，数据增强一直存在问题。本文通过对 3 个 TC 数据集使用数据包时间序列作为输入表示，并考虑多种训练条件，基准测试了 18 种增强函数，结果显示（i）DA 可以获得以前未探索的优势，（ii）基于时间序列顺序和掩码的增强函数更适用于 TC，（iii）简单的潜在空间分析可以提供有关增强效果的线索。

交通分类的数据增强

Data Augmentation for Traffic Classification

In an era where symbolic mathematical equations are indispensable for
modeling complex natural phenomena, scientific inquiry often involves
collecting observations and translating them into mathematical expressions.
Recently, deep learning has emerged as a powerful tool for extracting insights
from data. However, existing models typically specialize in either numeric or
symbolic domains, and are usually trained in a supervised manner tailored to
specific tasks. This approach neglects the substantial benefits that could
arise from a task-agnostic unified understanding between symbolic equations and
their numeric counterparts. To bridge the gap, we introduce SNIP, a
Symbolic-Numeric Integrated Pre-training, which employs joint contrastive
learning between symbolic and numeric domains, enhancing their mutual
similarities in the pre-trained embeddings. By performing latent space
analysis, we observe that SNIP provides cross-domain insights into the
representations, revealing that symbolic supervision enhances the embeddings of
numeric data and vice versa. We evaluate SNIP across diverse tasks, including
symbolic-to-numeric mathematical property prediction and numeric-to-symbolic
equation discovery, commonly known as symbolic regression. Results show that
SNIP effectively transfers to various tasks, consistently outperforming fully
supervised baselines and competing strongly with established task-specific
methods, especially in few-shot learning scenarios where available data is
limited.

在一个符号化数学方程与其数值对应物之间具有任务不可知的统一理解的显著益处的基础上，我们介绍了 SNIP，一种符号 - 数字集成预训练方法，通过使用符号领域和数字领域之间的联合对比学习来增强它们在预训练嵌入中的相似性。通过进行潜空间分析，我们观察到 SNIP 提供了对表示的跨领域见解，揭示了符号监督如何增强数字数据的嵌入以及反之。我们在包括符号到数值数学性质预测和数值到符号方程发现 (通常被称为符号回归) 在内的各种任务上评估了 SNIP。结果表明，SNIP 能够有效地应用于各种任务，在有限数据的少样本学习场景中始终优于全监督基线并与已确立的任务特定方法竞争激烈。