Streaming Linear Discriminant Analysis (LDA) while proven in
Class-incremental Learning deployments at the edge with limited classes (upto
1000), has not been proven for deployment in extreme classification scenarios.
In this paper, we present: (a) XLDA, a framework for Class-IL in edge
deployment where LDA classifier is proven to be equivalent to FC layer
including in extreme classification scenarios, and (b) optimizations to enable
XLDA-based training and inference for edge deployment where there is a
constraint on available compute resources. We show up to 42x speed up using a
batched training approach and up to 5x inference speedup with nearest neighbor
search on extreme datasets like AliProducts (50k classes) and Google Landmarks
V2 (81k classes)

在边缘部署的类增量学习中，流式线性判别分析（LDA）在有限类别的情况下（最多 1000 个）得到了证明，但对于极端分类场景的部署尚未得到证明。本文提出了 XLDA 框架，该框架在边缘部署中将 LDA 分类器证明与 FC 层等效，包括在极端分类场景中；并对可用的计算资源进行优化以实现基于 XLDA 的训练和推断。我们展示了使用批处理训练方法加速多达 42 倍，并在极端数据集（如 AliProducts（50k 类别）和 Google Landmarks V2（81k 类别））上通过最近邻搜索加速多达 5 倍的推断。

XLDA：边缘极端分类规模连续学习的线性判别分析

XLDA: Linear Discriminant Analysis for Scaling Continual Learning to  Extreme Classification at the Edge

While natural language processing systems often focus on a single language,
multilingual transfer learning has the potential to improve performance,
especially for low-resource languages. We introduce XLDA, cross-lingual data
augmentation, a method that replaces a segment of the input text with its
translation in another language. XLDA enhances performance of all 14 tested
languages of the cross-lingual natural language inference (XNLI) benchmark.
With improvements of up to $4.8\%$, training with XLDA achieves
state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast
to, and performs markedly better than, a more naive approach that aggregates
examples in various languages in a way that each example is solely in one
language. On the SQuAD question answering task, we see that XLDA provides a
$1.0\%$ performance increase on the English evaluation set. Comprehensive
experiments suggest that most languages are effective as cross-lingual
augmentors, that XLDA is robust to a wide range of translation quality, and
that XLDA is even more effective for randomly initialized models than for
pretrained models.

介绍了一种名为 XLDA 的跨语言数据增强方法，该方法通过替换输入文本中的部分内容为另一种语言的翻译来改善跨语言自然语言推理任务的性能，在 14 种语言的测试中均获得了提升，并在希腊语、土耳其语和乌尔都语方面取得了最好的性能，且远优于其他方法。