Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Sparse Visual Representations with Leaky Capped Norm
  Regularizers   Sparsity inducing regularization is an important part for learning
over-complete visual representations. Despite the popularity of $\ell_1$
regularization, in this paper, we investigate the usage of non-convex
regularizations in this problem. Our contribution consists of three parts.
First, we propose the leaky capped norm regularization (LCNR), which allows
model weights below a certain threshold to be regularized more strongly as
opposed to those above, therefore imposes strong sparsity and only introduces
controllable estimation bias. We propose a majorization-minimization algorithm
to optimize the joint objective function. Second, our study over monocular 3D
shape recovery and neural networks with LCNR outperforms $\ell_1$ and other
non-convex regularizations, achieving state-of-the-art performance and faster
convergence. Third, we prove a theoretical global convergence speed on the 3D
recovery problem. To the best of our knowledge, this is the first convergence
analysis of the 3D recovery problem.

14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics