The ability of deep image prior (DIP) to recover high-quality images from
incomplete or corrupted measurements has made it popular in inverse problems in
image restoration and medical imaging including magnetic resonance imaging
(MRI). However, conventional DIP suffers from severe overfitting and spectral
bias effects.In this work, we first provide an analysis of how DIP recovers
information from undersampled imaging measurements by analyzing the training
dynamics of the underlying networks in the kernel regime for different
architectures.This study sheds light on important underlying properties for
DIP-based recovery.Current research suggests that incorporating a reference
image as network input can enhance DIP's performance in image reconstruction
compared to using random inputs. However, obtaining suitable reference images
requires supervision, and raises practical difficulties. In an attempt to
overcome this obstacle, we further introduce a self-driven reconstruction
process that concurrently optimizes both the network weights and the input
while eliminating the need for training data. Our method incorporates a novel
denoiser regularization term which enables robust and stable joint estimation
of both the network input and reconstructed image.We demonstrate that our
self-guided method surpasses both the original DIP and modern supervised
methods in terms of MR image reconstruction performance and outperforms
previous DIP-based schemes for image inpainting.

通过分析深度图像先验（DIP）的训练动态，我们揭示了 DIP 基于核函数制度下从欠采样图像测量中恢复信息的重要属性；通过引入参考图像作为网络输入并结合新颖的去噪正则化项，我们提出了一种自引导重建方法，通过同时优化网络权重和输入，消除了训练数据的需求，并在磁共振图像重建方面超过了传统 DIP 和现代监督方法，并且在图像修补中也优于之前基于 DIP 的方案。

深度图像先验分析与图像重建中的自引导利用

Analysis of Deep Image Prior and Exploiting Self-Guidance for Image  Reconstruction

Deep learning based image classification models are shown vulnerable to
adversarial attacks by injecting deliberately crafted noises to clean images.
To defend against adversarial attacks in a training-free and attack-agnostic
manner, this work proposes a novel and effective reconstruction-based defense
framework by delving into deep image prior (DIP). Fundamentally different from
existing reconstruction-based defenses, the proposed method analyzes and
explicitly incorporates the model decision process into our defense. Given an
adversarial image, firstly we map its reconstructed images during DIP
optimization to the model decision space, where cross-boundary images can be
detected and on-boundary images can be further localized. Then, adversarial
noise is purified by perturbing on-boundary images along the reverse direction
to the adversarial image. Finally, on-manifold images are stitched to construct
an image that can be correctly predicted by the victim classifier. Extensive
experiments demonstrate that the proposed method outperforms existing
state-of-the-art reconstruction-based methods both in defending white-box
attacks and defense-aware attacks. Moreover, the proposed method can maintain a
high visual quality during adversarial image reconstruction.

针对深度学习图像分类模型易受恶意注入噪声的对抗攻击，本文基于深度图像先验提出了一种新颖有效的重建防御框架，并且分析和明确地将模型决策过程纳入了防御范畴，通过倒推清晰的 “干净样本”，最终构造出一幅可以被正确识别的图像，实验表明，在白盒、防御感知攻击下，该方法表现出优异的防御效果且重建图像的视觉质量较高。

探究基于深度图像先验的对抗防御：一种新颖的基于重建的防御框架

Delving into Deep Image Prior for Adversarial Defense: A Novel  Reconstruction-based Defense Framework

Data augmentation by mixing samples, such as Mixup, has widely been used
typically for classification tasks. However, this strategy is not always
effective due to the gap between augmented samples for training and original
samples for testing. This gap may prevent a classifier from learning the
optimal decision boundary and increase the generalization error. To overcome
this problem, we propose an alternative framework called Data Interpolating
Prediction (DIP). Unlike common data augmentations, we encapsulate the
sample-mixing process in the hypothesis class of a classifier so that train and
test samples are treated equally. We derive the generalization bound and show
that DIP helps to reduce the original Rademacher complexity. Also, we
empirically demonstrate that DIP can outperform existing Mixup.

提出了 Data Interpolating Prediction（DIP）框架，将样本混合过程与分类器的假设类封装在一起，从而使训练和测试样本受到平等对待，进而帮助降低 Rademacher 复杂度并减少泛化误差。DIP 在实证上表现比 Mixup 更优。