Deep learning models continue to advance in accuracy, yet they remain
vulnerable to adversarial attacks, which often lead to the misclassification of
adversarial examples. Adversarial training is used to mitigate this problem by
increasing robustness against these attacks. However, this approach typically
reduces a model's standard accuracy on clean, non-adversarial samples. The
necessity for deep learning models to balance both robustness and accuracy for
security is obvious, but achieving this balance remains challenging, and the
underlying reasons are yet to be clarified. This paper proposes a novel
adversarial training method called Adversarial Feature Alignment (AFA), to
address these problems. Our research unveils an intriguing insight:
misalignment within the feature space often leads to misclassification,
regardless of whether the samples are benign or adversarial. AFA mitigates this
risk by employing a novel optimization algorithm based on contrastive learning
to alleviate potential feature misalignment. Through our evaluations, we
demonstrate the superior performance of AFA. The baseline AFA delivers higher
robust accuracy than previous adversarial contrastive learning methods while
minimizing the drop in clean accuracy to 1.86% and 8.91% on CIFAR10 and
CIFAR100, respectively, in comparison to cross-entropy. We also show that joint
optimization of AFA and TRADES, accompanied by data augmentation using a recent
diffusion model, achieves state-of-the-art accuracy and robustness.

本文提出了一种名为 Adversarial Feature Alignment (AFA) 的新型对抗性训练方法，旨在解决深度学习模型在安全性上平衡鲁棒性和准确性的挑战。通过利用对比学习的优化算法，AFA 减轻特征不对齐可能导致误分类的风险，并在实验中展示了其卓越性能。

对抗特征对齐：通过对抗训练在深度学习中平衡鲁棒性和准确性

Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep  Learning via Adversarial Training

Deep learning based object detectors struggle generalizing to a new target
domain bearing significant variations in object and background. Most current
methods align domains by using image or instance-level adversarial feature
alignment. This often suffers due to unwanted background and lacks
class-specific alignment. A straightforward approach to promote class-level
alignment is to use high confidence predictions on unlabeled domain as
pseudo-labels. These predictions are often noisy since model is poorly
calibrated under domain shift. In this paper, we propose to leverage model's
predictive uncertainty to strike the right balance between adversarial feature
alignment and class-level alignment. We develop a technique to quantify
predictive uncertainty on class assignments and bounding-box predictions. Model
predictions with low uncertainty are used to generate pseudo-labels for
self-training, whereas the ones with higher uncertainty are used to generate
tiles for adversarial feature alignment. This synergy between tiling around
uncertain object regions and generating pseudo-labels from highly certain
object regions allows capturing both image and instance-level context during
the model adaptation. We report thorough ablation study to reveal the impact of
different components in our approach. Results on five diverse and challenging
adaptation scenarios show that our approach outperforms existing
state-of-the-art methods with noticeable margins.

利用模型的预测不确定性在领域迁移中实现对抗特征对齐和类别级对齐之间的平衡，从而在各种适应场景中超越现有的最先进方法。

领域自适应目标检测：自训练与对抗学习之间的平衡

Domain Adaptive Object Detection via Balancing Between Self-Training and  Adversarial Learning

Most existing domain adaptive object detection methods exploit adversarial
feature alignment to adapt the model to a new domain. Recent advances in
adversarial feature alignment strives to reduce the negative effect of
alignment, or negative transfer, that occurs because the distribution of
features varies depending on the category of objects. However, by analyzing the
features of the anchor-free one-stage detector, in this paper, we find that
negative transfer may occur because the feature distribution varies depending
on the regression value for the offset to the bounding box as well as the
category. To obtain domain invariance by addressing this issue, we align the
feature conditioned on the offset value, considering the modality of the
feature distribution. With a very simple and effective conditioning method, we
propose OADA (Offset-Aware Domain Adaptive object detector) that achieves
state-of-the-art performances in various experimental settings. In addition, by
analyzing through singular value decomposition, we find that our model enhances
both discriminability and transferability.

本论文提出了一种基于 OADA 的新型域自适应物体检测算法，通过考虑边界框偏移的特征条件化方法，解决了特征分布因物体种类和边界框偏移值而异的问题，并在实验中取得了最新颖的性能。

使用边框偏移量进行单阶段物体检测器的无监督领域适应

Unsupervised Domain Adaptation for One-stage Object Detector using  Offsets to Bounding Box

We study adapting trained object detectors to unseen domains manifesting
significant variations of object appearance, viewpoints and backgrounds. Most
current methods align domains by either using image or instance-level feature
alignment in an adversarial fashion. This often suffers due to the presence of
unwanted background and as such lacks class-specific alignment. A common remedy
to promote class-level alignment is to use high confidence predictions on the
unlabelled domain as pseudo labels. These high confidence predictions are often
fallacious since the model is poorly calibrated under domain shift. In this
paper, we propose to leverage model predictive uncertainty to strike the right
balance between adversarial feature alignment and class-level alignment.
Specifically, we measure predictive uncertainty on class assignments and the
bounding box predictions. Model predictions with low uncertainty are used to
generate pseudo-labels for self-supervision, whereas the ones with higher
uncertainty are used to generate tiles for an adversarial feature alignment
stage. This synergy between tiling around the uncertain object regions and
generating pseudo-labels from highly certain object regions allows us to
capture both the image and instance level context during the model adaptation
stage. We perform extensive experiments covering various domain shift
scenarios. Our approach improves upon existing state-of-the-art methods with
visible margins.

本文提出了一种利用预测不确定性来平衡对抗特征对齐和类级对齐的方法，以适应在物体外观、视角和背景方面存在显著变化的未知领域中的已训练的物体检测器。