Our paper addresses the problem of models struggling to learn diverse
features, due to either forgetting previously learned features or failing to
learn new ones. To overcome this problem, we introduce Diverse Feature Learning
(DFL), a method that combines an important feature preservation algorithm with
a new feature learning algorithm. Specifically, for preserving important
features, we utilize self-distillation in ensemble models by selecting the
meaningful model weights observed during training. For learning new features,
we employ reset that involves periodically re-initializing part of the model.
As a result, through experiments with various models on the image
classification, we have identified the potential for synergistic effects
between self-distillation and reset.

我们的研究解决了模型学习多样特征时面临的问题，通过引入多样特征学习（DFL）方法，结合重要特征保持算法和新特征学习算法。具体而言，为保留重要特征，我们使用自蒸馏在模型训练期间选择观察到的有意义的模型权重。为学习新特征，我们采用重置方法，定期重新初始化模型的一部分。通过在图像分类上进行各种模型的实验，我们发现自蒸馏和重置之间存在协同效应的潜力。

自我蒸馏与复位的多元特征学习

Diverse Feature Learning by Self-distillation and Reset

Denoising algorithms play a crucial role in medical image processing and
analysis. However, classical denoising algorithms often ignore explanatory and
critical medical features preservation, which may lead to misdiagnosis and
legal liabilities.In this work, we propose a new denoising method for medical
images that not only efficiently removes various types of noise, but also
preserves key medical features throughout the process. To achieve this goal, we
utilize a gradient-based eXplainable Artificial Intelligence (XAI) approach to
design a feature preserving loss function. Our feature preserving loss function
is motivated by the characteristic that gradient-based XAI is sensitive to
noise. Through backpropagation, medical image features before and after
denoising can be kept consistent. We conducted extensive experiments on three
available medical image datasets, including synthesized 13 different types of
noise and artifacts. The experimental results demonstrate the superiority of
our method in terms of denoising performance, model explainability, and
generalization.

提出了一种新的医学图像去噪方法，该方法不仅能有效去除各种类型的噪声，还能在整个过程中保持关键的医学特征。通过梯度上升的可解释人工智能方法设计了一种特征保留损失函数，实验证明我们的方法在去噪性能、模型可解释性和泛化能力方面具有显著优势。

基于可解释人工智能特征保持损失的医学图像去噪

Medical Image Denosing via Explainable AI Feature Preserving Loss

Existing normal estimation methods for point clouds are often less robust to
severe noise and complex geometric structures. Also, they usually ignore the
contributions of different neighbouring points during normal estimation, which
leads to less accurate results. In this paper, we introduce a weighted normal
estimation method for 3D point cloud data. We innovate in two key points: 1) we
develop a novel weighted normal regression technique that predicts point-wise
weights from local point patches and use them for robust, feature-preserving
normal regression; 2) we propose to conduct contrastive learning between point
patches and the corresponding ground-truth normals of the patches' central
points as a pre-training process to facilitate normal regression. Comprehensive
experiments demonstrate that our method can robustly handle noisy and complex
point clouds, achieving state-of-the-art performance on both synthetic and
real-world datasets.

本研究提出了一种加权法线估算方法，包括一种新型加权法线回归技术和一项对比度学习预处理过程，在保持特征的同时实现了鲁棒性，处理了噪声和复杂的点云，获得了合成和真实数据集上的最先进性能。