5,616 research outputs found
VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
One fundamental challenge of vehicle re-identification (re-id) is to learn
robust and discriminative visual representation, given the significant
intra-class vehicle variations across different camera views. As the existing
vehicle datasets are limited in terms of training images and viewpoints, we
propose to build a unique large-scale vehicle dataset (called VehicleNet) by
harnessing four public vehicle datasets, and design a simple yet effective
two-stage progressive approach to learning more robust visual representation
from VehicleNet. The first stage of our approach is to learn the generic
representation for all domains (i.e., source vehicle datasets) by training with
the conventional classification loss. This stage relaxes the full alignment
between the training and testing domains, as it is agnostic to the target
vehicle domain. The second stage is to fine-tune the trained model purely based
on the target vehicle set, by minimizing the distribution discrepancy between
our VehicleNet and any target domain. We discuss our proposed multi-source
dataset VehicleNet and evaluate the effectiveness of the two-stage progressive
representation learning through extensive experiments. We achieve the
state-of-art accuracy of 86.07% mAP on the private test set of AICity
Challenge, and competitive results on two other public vehicle re-id datasets,
i.e., VeRi-776 and VehicleID. We hope this new VehicleNet dataset and the
learned robust representations can pave the way for vehicle re-id in the
real-world environments
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
The ever-growing complexity of reinforcement learning (RL) tasks demands a
distributed RL system to efficiently generate and process a massive amount of
data to train intelligent agents. However, existing open-source libraries
suffer from various limitations, which impede their practical use in
challenging scenarios where large-scale training is necessary. While industrial
systems from OpenAI and DeepMind have achieved successful large-scale RL
training, their system architecture and implementation details remain
undisclosed to the community. In this paper, we present a novel abstraction on
the dataflows of RL training, which unifies practical RL training across
diverse applications into a general framework and enables fine-grained
optimizations. Following this abstraction, we develop a scalable, efficient,
and extensible distributed RL system called ReaLly Scalable RL (SRL). The
system architecture of SRL separates major RL computation components and allows
massively parallelized training. Moreover, SRL offers user-friendly and
extensible interfaces for customized algorithms. Our evaluation shows that SRL
outperforms existing academic libraries in both a single machine and a
medium-sized cluster. In a large-scale cluster, the novel architecture of SRL
leads to up to 3.7x speedup compared to the design choices adopted by the
existing libraries. We also conduct a direct benchmark comparison to OpenAI's
industrial system, Rapid, in the challenging hide-and-seek environment. SRL
reproduces the same solution as reported by OpenAI with up to 5x speedup in
wall-clock time. Furthermore, we also examine the performance of SRL in a much
harder variant of the hide-and-seek environment and achieve substantial
learning speedup by scaling SRL to over 15k CPU cores and 32 A100 GPUs.
Notably, SRL is the first in the academic community to perform RL experiments
at such a large scale.Comment: 15 pages, 12 figures, 6 table
An Efficient Management System for Wireless Sensor Networks
Wireless sensor networks have garnered considerable attention recently. Networks typically have many sensor nodes, and are used in commercial, medical, scientific, and military applications for sensing and monitoring the physical world. Many researchers have attempted to improve wireless sensor network management efficiency. A Simple Network Management Protocol (SNMP)-based sensor network management system was developed that is a convenient and effective way for managers to monitor and control sensor network operations. This paper proposes a novel WSNManagement system that can show the connections stated of relationships among sensor nodes and can be used for monitoring, collecting, and analyzing information obtained by wireless sensor networks. The proposed network management system uses collected information for system configuration. The function of performance analysis facilitates convenient management of sensors. Experimental results show that the proposed method enhances the alive rate of an overall sensor node system, reduces the packet lost rate by roughly 5%, and reduces delay time by roughly 0.2 seconds. Performance analysis demonstrates that the proposed system is effective for wireless sensor network management
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