Although augmentations (e.g., perturbation of graph edges, image crops) boost
the efficiency of Contrastive Learning (CL), feature level augmentation is
another plausible, complementary yet not well researched strategy. Thus, we
present a novel spectral feature argumentation for contrastive learning on
graphs (and images). To this end, for each data view, we estimate a low-rank
approximation per feature map and subtract that approximation from the map to
obtain its complement. This is achieved by the proposed herein incomplete power
iteration, a non-standard power iteration regime which enjoys two valuable
byproducts (under mere one or two iterations): (i) it partially balances
spectrum of the feature map, and (ii) it injects the noise into rebalanced
singular values of the feature map (spectral augmentation). For two views, we
align these rebalanced feature maps as such an improved alignment step can
focus more on less dominant singular values of matrices of both views, whereas
the spectral augmentation does not affect the spectral angle alignment
(singular vectors are not perturbed). We derive the analytical form for: (i)
the incomplete power iteration to capture its spectrum-balancing effect, and
(ii) the variance of singular values augmented implicitly by the noise. We also
show that the spectral augmentation improves the generalization bound.
Experiments on graph/image datasets show that our spectral feature augmentation
outperforms baselines, and is complementary with other augmentation strategies
and compatible with various contrastive losses.

本文介绍了一种用于对图和图片进行对比学习的新颖的光谱特征增强方法，通过不完全的幂迭代，可以部分平衡特征图的频谱，注入噪声，从而实现光谱增强，提高了泛化能力，是一种与其他增强策略互补且兼容各种对比损失的方法。

图对比学习及其它领域的频谱特征增强

Spectral Feature Augmentation for Graph Contrastive Learning and Beyond

Graph contrastive learning (GCL), as an emerging self-supervised learning
technique on graphs, aims to learn representations via instance discrimination.
Its performance heavily relies on graph augmentation to reflect invariant
patterns that are robust to small perturbations; yet it still remains unclear
about what graph invariance GCL should capture. Recent studies mainly perform
topology augmentations in a uniformly random manner in the spatial domain,
ignoring its influence on the intrinsic structural properties embedded in the
spectral domain. In this work, we aim to find a principled way for topology
augmentations by exploring the invariance of graphs from the spectral
perspective. We develop spectral augmentation which guides topology
augmentations by maximizing the spectral change. Extensive experiments on both
graph and node classification tasks demonstrate the effectiveness of our method
in self-supervised representation learning. The proposed method also brings
promising generalization capability in transfer learning, and is equipped with
intriguing robustness property under adversarial attacks. Our study sheds light
on a general principle for graph topology augmentation.

本研究提出了一种基于光谱扩增的拓扑扩充方法，可以指导 GCL 捕获特征的不变性，实现了自监督表征学习，并带来了更好的泛化能力和抵御对抗性攻击的鲁棒性。

图上自监督学习的谱增强

Spectral Augmentation for Self-Supervised Learning on Graphs

Spoken language Identification (LID) systems are needed to identify the
language(s) present in a given audio sample, and typically could be the first
step in many speech processing related tasks such as automatic speech
recognition (ASR). Automatic identification of the languages present in a
speech signal is not only scientifically interesting, but also of practical
importance in a multilingual country such as India. In many of the Indian
cities, when people interact with each other, as many as three languages may
get mixed. These may include the official language of that province, Hindi and
English (at times the languages of the neighboring provinces may also get mixed
during these interactions). This makes the spoken LID task extremely
challenging in Indian context. While quite a few LID systems in the context of
Indian languages have been implemented, most such systems have used small scale
speech data collected internally within an organization. In the current work,
we perform spoken LID on three Indian languages (Gujarati, Telugu, and Tamil)
code-mixed with English. This task was organized by the Microsoft research team
as a spoken LID challenge. In our work, we modify the usual spectral
augmentation approach and propose a language mask that discriminates the
language ID pairs, which leads to a noise robust spoken LID system. The
proposed method gives a relative improvement of approximately 3-5% in the LID
accuracy over a baseline system proposed by Microsoft on the three language
pairs for two shared tasks suggested in the challenge.

本研究针对印度语境下多种语言混杂的情况，对在语音处理中较为基础的语种识别系统 LID 进行优化，提出基于语种掩蔽和光谱增强的方法，在微软研究团队提出的挑战任务中相对基线系统进行了 3-5% 的 LID 准确度改进。