Many image processing tasks involve image-to-image mapping, which can be
addressed well by fully convolutional networks (FCN) without any heavy
preprocessing. Although empirically designing and training FCNs can achieve
satisfactory results, reasons for the improvement in performance are slightly
ambiguous. Our study is to make progress in understanding their generalization
abilities through visualizing the optimization landscapes. The visualization of
objective functions is obtained by choosing a solution and projecting its
vicinity onto a 3D space. We compare three FCN-based networks (two existing
models and a new proposed in this paper for comparison) on multiple datasets.
It has been observed in practice that the connections from the pre-pooled
feature maps to the post-upsampled can achieve better results. We investigate
the cause and provide experiments to shows that the skip-layer connections in
FCN can promote flat optimization landscape, which is well known to generalize
better. Additionally, we explore the relationship between the models
generalization ability and loss surface under different batch sizes. Results
show that large-batch training makes the model converge to sharp minimizers
with chaotic vicinities while small-batch method leads the model to flat
minimizers with smooth and nearly convex regions. Our work may contribute to
insights and analysis for designing and training FCNs.

本文通过可视化优化地形图，比较了三个基于 FCN 的模型在多个数据集上的性能，并探索了 FCN 中跳过层连接与模型广义能力之间的关系，以及不同批量大小下的损失表面与模型广义能力的关系。

完全卷积网络优化空间的可视化洞察

Visualized Insights into the Optimization Landscape of Fully  Convolutional Networks

Image restoration, including image denoising, super resolution, inpainting,
and so on, is a well-studied problem in computer vision and image processing,
as well as a test bed for low-level image modeling algorithms. In this work, we
propose a very deep fully convolutional auto-encoder network for image
restoration, which is a encoding-decoding framework with symmetric
convolutional-deconvolutional layers. In other words, the network is composed
of multiple layers of convolution and de-convolution operators, learning
end-to-end mappings from corrupted images to the original ones. The
convolutional layers capture the abstraction of image contents while
eliminating corruptions. Deconvolutional layers have the capability to upsample
the feature maps and recover the image details. To deal with the problem that
deeper networks tend to be more difficult to train, we propose to symmetrically
link convolutional and deconvolutional layers with skip-layer connections, with
which the training converges much faster and attains better results.

本文提出一种使用对称卷积 - 反卷积层的深度全卷积自编码器网络用于图像修复，通过跳级连接缓解深度网络训练难的问题，达到了更快收敛和更好的修复效果。

使用具有对称跳跃连接的卷积自编码器进行图像恢复

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip  Connections

In this paper, we propose a very deep fully convolutional encoding-decoding
framework for image restoration such as denoising and super-resolution. The
network is composed of multiple layers of convolution and de-convolution
operators, learning end-to-end mappings from corrupted images to the original
ones. The convolutional layers act as the feature extractor, which capture the
abstraction of image contents while eliminating noises/corruptions.
De-convolutional layers are then used to recover the image details. We propose
to symmetrically link convolutional and de-convolutional layers with skip-layer
connections, with which the training converges much faster and attains a
higher-quality local optimum. First, The skip connections allow the signal to
be back-propagated to bottom layers directly, and thus tackles the problem of
gradient vanishing, making training deep networks easier and achieving
restoration performance gains consequently. Second, these skip connections pass
image details from convolutional layers to de-convolutional layers, which is
beneficial in recovering the original image. Significantly, with the large
capacity, we can handle different levels of noises using a single model.
Experimental results show that our network achieves better performance than all
previously reported state-of-the-art methods.

本文提出了一种具有对称连接多层卷积和反卷积操作的深度全卷积编码 - 解码框架，通过跳层连接缓解了梯度消失的问题，实现了优越的图像修复性能。