论文标题
解决混合深度图像先验的反问题：防止过度拟合的挑战
Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfitting
论文作者
论文摘要
我们主要分析和解决深层图像先验的过度拟合问题（DIP）。深度图像先验可以解决诸如超分辨率，介入和降解等反问题。与其他深度学习方法相比，DIP的主要优点是它不需要访问大型数据集。但是，由于神经网络和嘈杂数据的参数数量大量，随着迭代次数的增长，图像中的噪声倾斜。在论文中，我们使用混合深层图像先验来避免过度拟合。杂种先验是将浸入与明确的先验相结合，例如总变异或隐式先验，例如denoising算法。我们使用交替的方向方法（ADMM）合并新的先验并尝试不同形式的ADMM，以避免由ADMM步骤的内部循环引起的额外计算。我们还研究了梯度下降的动力学与过度拟合现象之间的关系。数值结果表明，混合培训在防止过度拟合中起着重要作用。此外，我们尝试沿某些方向拟合图像，发现此方法可以减少噪声水平较大时的过度拟合。当噪声水平很小时，它不会大大减少过度拟合的问题。
We mainly analyze and solve the overfitting problem of deep image prior (DIP). Deep image prior can solve inverse problems such as super-resolution, inpainting and denoising. The main advantage of DIP over other deep learning approaches is that it does not need access to a large dataset. However, due to the large number of parameters of the neural network and noisy data, DIP overfits to the noise in the image as the number of iterations grows. In the thesis, we use hybrid deep image priors to avoid overfitting. The hybrid priors are to combine DIP with an explicit prior such as total variation or with an implicit prior such as a denoising algorithm. We use the alternating direction method-of-multipliers (ADMM) to incorporate the new prior and try different forms of ADMM to avoid extra computation caused by the inner loop of ADMM steps. We also study the relation between the dynamics of gradient descent, and the overfitting phenomenon. The numerical results show the hybrid priors play an important role in preventing overfitting. Besides, we try to fit the image along some directions and find this method can reduce overfitting when the noise level is large. When the noise level is small, it does not considerably reduce the overfitting problem.
