The deep model training procedure requires large-scale datasets of annotated
data. Due to the difficulty of annotating a large number of samples, label
noise caused by incorrect annotations is inevitable, resulting in low model
performance and poor model generalization. To combat label noise, current
methods usually select clean samples based on the small-loss criterion and use
these samples for training. Due to some noisy samples similar to clean ones,
these small-loss criterion-based methods are still affected by label noise. To
address this issue, in this work, we propose Regroup Median Loss (RML) to
reduce the probability of selecting noisy samples and correct losses of noisy
samples. RML randomly selects samples with the same label as the training
samples based on a new loss processing method. Then, we combine the stable mean
loss and the robust median loss through a proposed regrouping strategy to
obtain robust loss estimation for noisy samples. To further improve the model
performance against label noise, we propose a new sample selection strategy and
build a semi-supervised method based on RML. Compared to state-of-the-art
methods, for both the traditionally trained and semi-supervised models, RML
achieves a significant improvement on synthetic and complex real-world
datasets. The source code of the paper has been released.

RML 是一种用于降低选择噪声样本概率和校正噪声样本损失的方法，通过稳定的均值损失和健壮的中值损失组合以获得噪声样本的鲁棒损失估计，并提出了新的样本选择策略和基于 RML 的半监督方法来进一步提高模型对标签噪声的性能。

应对标签噪声的重新分组中值损失

Regroup Median Loss for Combating Label Noise

As the number of parameters in Deep Neural Networks (DNNs) scales, the thirst
for training data also increases. To save costs, it has become common for users
and enterprises to delegate time-consuming data collection to third parties.
Unfortunately, recent research has shown that this practice raises the risk of
DNNs being exposed to backdoor attacks. Specifically, an attacker can
maliciously control the behavior of a trained model by poisoning a small
portion of the training data. In this study, we focus on improving the
poisoning efficiency of backdoor attacks from the sample selection perspective.
The existing attack methods construct such poisoned samples by randomly
selecting some clean data from the benign set and then embedding a trigger into
them. However, this random selection strategy ignores that each sample may
contribute differently to the backdoor injection, thereby reducing the
poisoning efficiency. To address the above problem, a new selection strategy
named Improved Filtering and Updating Strategy (FUS++) is proposed.
Specifically, we adopt the forgetting events of the samples to indicate the
contribution of different poisoned samples and use the curvature of the loss
surface to analyses the effectiveness of this phenomenon. Accordingly, we
combine forgetting events and curvature of different samples to conduct a
simple yet efficient sample selection strategy. The experimental results on
image classification (CIFAR-10, CIFAR-100, ImageNet-10), text classification
(AG News), audio classification (ESC-50), and age regression (Facial Age)
consistently demonstrate the effectiveness of the proposed strategy: the attack
performance using FUS++ is significantly higher than that using random
selection for the same poisoning ratio.

本研究重点改进了从样本选择角度提高后门攻击的注入效率，并提出了一种名为改进过滤和更新策略（FUS++）的选择策略，实验证明该策略的攻击性能显著高于随机选择的攻击性能。

探索数据选择对后门攻击中的毒性效果的影响

Explore the Effect of Data Selection on Poison Efficiency in Backdoor  Attacks

Transfer learning for extremely low resource languages is a challenging task
as there is no large scale monolingual corpora for pre training or sufficient
annotated data for fine tuning. We follow the work of MetaXL which suggests
using meta learning for transfer learning from a single source language to an
extremely low resource one. We propose an enhanced approach which uses multiple
source languages chosen in a data driven manner. In addition, we introduce a
sample selection strategy for utilizing the languages in training by using a
multi armed bandit algorithm. Using both of these improvements we managed to
achieve state of the art results on the NER task for the extremely low resource
languages while using the same amount of data, making the representations
better generalized. Also, due to the method ability to use multiple languages
it allows the framework to use much larger amounts of data, while still having
superior results over the former MetaXL method even with the same amounts of
data.

本研究针对极低资源语言的转移学习提出了多源语言和样本选择策略的增强方法，实现了在使用相同数量数据的情况下 NER 任务的最新成果。