1,214 research outputs found
SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration
The study of sparsity in Convolutional Neural Networks (CNNs) has become
widespread to compress and accelerate models in environments with limited
resources. By constraining N consecutive weights along the output channel to be
group-wise non-zero, the recent network with 1N sparsity has received
tremendous popularity for its three outstanding advantages: 1) A large amount
of storage space saving by a \emph{Block Sparse Row} matrix. 2) Excellent
performance at a high sparsity. 3) Significant speedups on CPUs with Advanced
Vector Extensions. Recent work requires selecting and fine-tuning 1N
sparse weights based on dense pre-trained weights, leading to the problems such
as expensive training cost and memory access, sub-optimal model quality, as
well as unbalanced workload across threads (different sparsity across output
channels). To overcome them, this paper proposes a novel \emph{\textbf{S}oft
\textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a
uniform 1N sparse structured network from scratch. Specifically, our
approach tends to repeatedly allow pruned blocks to regrow to the network based
on block angular redundancy and importance sampling in a uniform manner
throughout the training process. It not only makes the model less dependent on
pre-training, reduces the model redundancy and the risk of pruning the
important blocks permanently but also achieves balanced workload. Empirically,
on ImageNet, comprehensive experiments across various CNN architectures show
that our SUBP consistently outperforms existing 1N and structured
sparsity methods based on pre-trained models or training from scratch. Source
codes and models are available at \url{https://github.com/JingyangXiang/SUBP}.Comment: 14 pages, 4 figures, Accepted by 37th Conference on Neural
Information Processing Systems (NeurIPS 2023
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
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