The classical sparse coding (SC) model represents visual stimuli as a linear
combination of a handful of learned basis functions that are Gabor-like when
trained on natural image data. However, the Gabor-like filters learned by
classical sparse coding far overpredict well-tuned simple cell receptive field
profiles observed empirically. While neurons fire sparsely, neuronal
populations are also organized in physical space by their sensitivity to
certain features. In V1, this organization is a smooth progression of
orientations along the cortical sheet. A number of subsequent models have
either discarded the sparse dictionary learning framework entirely or whose
updates have yet to take advantage of the surge in unrolled, neural dictionary
learning architectures. A key missing theme of these updates is a stronger
notion of \emph{structured sparsity}. We propose an autoencoder architecture
(WLSC) whose latent representations are implicitly, locally organized for
spectral clustering through a Laplacian quadratic form of a bipartite graph,
which generates a diverse set of artificial receptive fields that match primate
data in V1 as faithfully as recent contrastive frameworks like Local Low
Dimensionality, or LLD \citep{lld} that discard sparse dictionary learning. By
unifying sparse and smooth coding in models of the early visual cortex through
our autoencoder, we also show that our regularization can be interpreted as
early-stage specialization of receptive fields to certain classes of stimuli;
that is, we induce a weak clustering bias for later stages of cortex where
functional and spatial segregation (i.e. topography) are known to occur. The
results show an imperative for \emph{spatial regularization} of both the
receptive fields and firing rates to begin to describe feature disentanglement
in V1 and beyond.

在这篇论文中，我们提出了一种自动编码器架构（WLSC），其潜在表示通过二分图的拉普拉斯二次形式实现了隐式的、本地的谱聚类，生成了一组多样的人工感受野，与 V1 的灵长类动物数据具有相近的一致性，而且也展示了我们的正则化可以被解释为感受野对特定刺激类别的早期专门化，即我们为之后的皮质阶段引入了弱的聚类偏差，这是已知的功能性和空间分离（即拓扑）发生的地方。这些结果表明，在对 V1 及其之后的特征分离进行描述时，对感受野和放电率进行空间正则化至关重要。

使用展开网络进行聚类归纳偏差

Clustering Inductive Biases with Unrolled Networks

When adopting a model-based formulation, solving inverse problems encountered
in multiband imaging requires to define spatial and spectral regularizations.
In most of the works of the literature, spectral information is extracted from
the observations directly to derive data-driven spectral priors. Conversely,
the choice of the spatial regularization often boils down to the use of
conventional penalizations (e.g., total variation) promoting expected features
of the reconstructed image (e.g., piecewise constant). In this work, we propose
a generic framework able to capitalize on an auxiliary acquisition of high
spatial resolution to derive tailored data-driven spatial regularizations. This
approach leverages on the ability of deep learning to extract high level
features. More precisely, the regularization is conceived as a deep generative
network able to encode spatial semantic features contained in this auxiliary
image of high spatial resolution. To illustrate the versatility of this
approach, it is instantiated to conduct two particular tasks, namely multiband
image fusion and multiband image inpainting. Experimental results obtained on
these two tasks demonstrate the benefit of this class of informed
regularizations when compared to more conventional ones.

本文提出了一种使用辅助高分辨率图像提取定制数据驱动空间正则化的通用框架，并将其用于多波段图像融合和修复任务中，实验结果表明与传统方法相比，具有更好的效果。

基于引导深度生成模型的空间正则化多波段成像反问题

Guided Deep Generative Model-based Spatial Regularization for Multiband  Imaging Inverse Problems

Robust and accurate visual tracking is one of the most challenging computer
vision problems. Due to the inherent lack of training data, a robust approach
for constructing a target appearance model is crucial. Recently,
discriminatively learned correlation filters (DCF) have been successfully
applied to address this problem for tracking. These methods utilize a periodic
assumption of the training samples to efficiently learn a classifier on all
patches in the target neighborhood. However, the periodic assumption also
introduces unwanted boundary effects, which severely degrade the quality of the
tracking model.
We propose Spatially Regularized Discriminative Correlation Filters (SRDCF)
for tracking. A spatial regularization component is introduced in the learning
to penalize correlation filter coefficients depending on their spatial
location. Our SRDCF formulation allows the correlation filters to be learned on
a significantly larger set of negative training samples, without corrupting the
positive samples. We further propose an optimization strategy, based on the
iterative Gauss-Seidel method, for efficient online learning of our SRDCF.
Experiments are performed on four benchmark datasets: OTB-2013, ALOV++,
OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all
four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and
8.2% respectively, in mean overlap precision, compared to the best existing
trackers.

介绍了 Spatially Regularized Discriminative Correlation Filters (SRDCF) 算法用于解决缺乏训练数据的视觉追踪问题，并且提出了一种空间正则化组件，能够更高效地学习目标外观模型。在四个基准测试数据集上进行了实验，优于已有最佳追踪器，并在 OTB-2013 和 OTB-2015 中获得了较高的平均重叠精度。