Learning with noisy labels aims to ensure model generalization given a
label-corrupted training set. The sample selection strategy achieves promising
performance by selecting a label-reliable subset for model training. In this
paper, we empirically reveal that existing sample selection methods suffer from
both data and training bias that are represented as imbalanced selected sets
and accumulation errors in practice, respectively. However, only the training
bias was handled in previous studies. To address this limitation, we propose a
noIse-Tolerant Expert Model (ITEM) for debiased learning in sample selection.
Specifically, to mitigate the training bias, we design a robust network
architecture that integrates with multiple experts. Compared with the
prevailing double-branch network, our network exhibits better performance of
selection and prediction by ensembling these experts while training with fewer
parameters. Meanwhile, to mitigate the data bias, we propose a mixed sampling
strategy based on two weight-based data samplers. By training on the mixture of
two class-discriminative mini-batches, the model mitigates the effect of the
imbalanced training set while avoiding sparse representations that are easily
caused by sampling strategies. Extensive experiments and analyses demonstrate
the effectiveness of ITEM. Our code is available at this url
\href{this https URL}{ITEM}.

在这篇论文中，我们揭示了现有的样本选择方法在实践中存在的数据和训练偏差问题，并提出了一种鲁棒的网络架构和混合采样策略来解决这些问题，以实现对带有噪声标签的学习任务的准确建模。

对抗噪声标签的无偏样本选择

Debiased Sample Selection for Combating Noisy Labels

It has been shown that deep neural networks are prone to overfitting on
biased training data. Towards addressing this issue, meta-learning employs a
meta model for correcting the training bias. Despite the promising
performances, super slow training is currently the bottleneck in the meta
learning approaches. In this paper, we introduce a novel Faster Meta Update
Strategy (FaMUS) to replace the most expensive step in the meta gradient
computation with a faster layer-wise approximation. We empirically find that
FaMUS yields not only a reasonably accurate but also a low-variance
approximation of the meta gradient. We conduct extensive experiments to verify
the proposed method on two tasks. We show our method is able to save two-thirds
of the training time while still maintaining the comparable or achieving even
better generalization performance. In particular, our method achieves the
state-of-the-art performance on both synthetic and realistic noisy labels, and
obtains promising performance on long-tailed recognition on standard
benchmarks.

本研究提出了一种 Faster Meta Update Strategy（FaMUS）来解决 meta-learning 中训练速度慢的问题，该方法能够在保持可比性或实现更好的泛化性能的同时，节省三分之二的训练时间，并在标准基准上实现了长尾识别的良好性能。

快速元更新策略用于抗噪深度学习

Faster Meta Update Strategy for Noise-Robust Deep Learning

Today's scene graph generation (SGG) task is still far from practical, mainly
due to the severe training bias, e.g., collapsing diverse "human walk on / sit
on / lay on beach" into "human on beach". Given such SGG, the down-stream tasks
such as VQA can hardly infer better scene structures than merely a bag of
objects. However, debiasing in SGG is not trivial because traditional debiasing
methods cannot distinguish between the good and bad bias, e.g., good context
prior (e.g., "person read book" rather than "eat") and bad long-tailed bias
(e.g., "near" dominating "behind / in front of"). In this paper, we present a
novel SGG framework based on causal inference but not the conventional
likelihood. We first build a causal graph for SGG, and perform traditional
biased training with the graph. Then, we propose to draw the counterfactual
causality from the trained graph to infer the effect from the bad bias, which
should be removed. In particular, we use Total Direct Effect (TDE) as the
proposed final predicate score for unbiased SGG. Note that our framework is
agnostic to any SGG model and thus can be widely applied in the community who
seeks unbiased predictions. By using the proposed Scene Graph Diagnosis toolkit
on the SGG benchmark Visual Genome and several prevailing models, we observed
significant improvements over the previous state-of-the-art methods.

本文通过因果推断方法构建了一个场景图生成的因果图，并通过传统偏差训练和反事实因果关系推断方法去除其不良偏差，最终提出了一个基于 Total Direct Effect 的无偏差 SGG 框架。

从有偏训练数据中生成无偏场景图

Unbiased Scene Graph Generation from Biased Training

Example weighting algorithm is an effective solution to the training bias
problem, however, most previous typical methods are usually limited to human
knowledge and require laborious tuning of hyperparameters. In this paper, we
propose a novel example weighting framework called Learning to Auto Weight
(LAW). The proposed framework finds step-dependent weighting policies
adaptively, and can be jointly trained with target networks without any
assumptions or prior knowledge about the dataset. It consists of three key
components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge
searching space in a complete training process; Duplicate Network Reward (DNR)
gives more accurate supervision by removing randomness during the searching
process; Full Data Update (FDU) further improves the updating efficiency.
Experimental results demonstrate the superiority of weighting policy explored
by LAW over standard training pipeline. Compared with baselines, LAW can find a
better weighting schedule which achieves much more superior accuracy on both
biased CIFAR and ImageNet.

本文提出的学习自动加权（LAW）框架是一种有效的解决训练偏差问题的新型示例加权方法，采用三个关键组件，通过阶段搜索、重复网络奖励、全数据更新等操作实现自适应的加权方案，实验结果证明其优于标准训练流程，可在倾斜的 CIFAR 和 ImageNet 中找到更好的加权计划提高准确率。