493 research outputs found
Support Neighbor Loss for Person Re-Identification
Person re-identification (re-ID) has recently been tremendously boosted due
to the advancement of deep convolutional neural networks (CNN). The majority of
deep re-ID methods focus on designing new CNN architectures, while less
attention is paid on investigating the loss functions. Verification loss and
identification loss are two types of losses widely used to train various deep
re-ID models, both of which however have limitations. Verification loss guides
the networks to generate feature embeddings of which the intra-class variance
is decreased while the inter-class ones is enlarged. However, training networks
with verification loss tends to be of slow convergence and unstable performance
when the number of training samples is large. On the other hand, identification
loss has good separating and scalable property. But its neglect to explicitly
reduce the intra-class variance limits its performance on re-ID, because the
same person may have significant appearance disparity across different camera
views. To avoid the limitations of the two types of losses, we propose a new
loss, called support neighbor (SN) loss. Rather than being derived from data
sample pairs or triplets, SN loss is calculated based on the positive and
negative support neighbor sets of each anchor sample, which contain more
valuable contextual information and neighborhood structure that are beneficial
for more stable performance. To ensure scalability and separability, a
softmax-like function is formulated to push apart the positive and negative
support sets. To reduce intra-class variance, the distance between the anchor's
nearest positive neighbor and furthest positive sample is penalized.
Integrating SN loss on top of Resnet50, superior re-ID results to the
state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201
Orbital Stability of Solitary Waves for Generalized Symmetric Regularized-Long-Wave Equations with Two Nonlinear Terms
This paper investigates the orbital stability of solitary waves for the generalized symmetric regularized-long-wave equations with two nonlinear terms and analyzes the influence of the interaction between two nonlinear terms on the orbital stability. Since J is not onto, Grillakis-Shatah-Strauss theory cannot be applied on the system directly. We overcome this difficulty and obtain the general conclusion on orbital stability of solitary waves in this paper. Then, according to two exact solitary waves of the equations, we deduce the explicit expression of discrimination d′′(c) and give several sufficient conditions which can be used to judge the orbital stability and instability for the two solitary waves. Furthermore, we analyze the influence of the interaction between two nonlinear terms of the equations on the wave speed interval which makes the solitary waves stable
Trust model for certificate revocation in Ad hoc networks
In this paper we propose a distributed trust model for certificate revocation in Adhoc networks. The proposed model allows trust to be built over time as the number of interactions between nodes increase. Furthermore, trust in a node is defined not only in terms of its potential for maliciousness, but also in terms of the quality of the service it provides. Trust in nodes where there is little or no history of interactions is determined by recommendations from other nodes. If the nodes in the network are selfish, trust is obtained by an exchange of portfolios. Bayesian networks form the underlying basis for this model
Can Domain Adaptation Improve Accuracy and Fairness of Skin Lesion Classification?
Deep learning-based diagnostic system has demonstrated potential in
classifying skin cancer conditions when labeled training example are abundant.
However, skin lesion analysis often suffers from a scarcity of labeled data,
hindering the development of an accurate and reliable diagnostic system. In
this work, we leverage multiple skin lesion datasets and investigate the
feasibility of various unsupervised domain adaptation (UDA) methods in binary
and multi-class skin lesion classification. In particular, we assess three UDA
training schemes: single-, combined-, and multi-source. Our experiment results
show that UDA is effective in binary classification, with further improvement
being observed when imbalance is mitigated. In multi-class task, its
performance is less prominent, and imbalance problem again needs to be
addressed to achieve above-baseline accuracy. Through our quantitative
analysis, we find that the test error of multi-class tasks is strongly
correlated with label shift, and feature-level UDA methods have limitations
when handling imbalanced datasets. Finally, our study reveals that UDA can
effectively reduce bias against minority groups and promote fairness, even
without the explicit use of fairness-focused techniques
Load Characteristics of Wireless Power Transfer System with Different Resonant Types and Resonator Numbers
Wireless Power Transfer (WPT) has been the research focus and applied in many fields. Normally power is transferred wirelessly to charge the battery, which requires specific load characteristics. The load characteristics are essential for the design and operation of the WPT system. This paper investigates the load characteristics of the WPT system with different resonant types and resonator numbers. It is found that in a WPT system with series or LCL resonance under a constant voltage source, the load characteristic is determined by the number of inductors. Even number of inductors results in a constant current characteristic and odd number constant voltage characteristic. Calculations, simulations, and experiments verify the analysis
Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
Network slicing-based communication systems can dynamically and efficiently
allocate resources for diversified services. However, due to the limitation of
the network interface on channel access and the complexity of the resource
allocation, it is challenging to achieve an acceptable solution in the
practical system without precise prior knowledge of the dynamics probability
model of the service requests. Existing work attempts to solve this problem
using deep reinforcement learning (DRL), however, such methods usually require
a lot of interaction with the real environment in order to achieve good
results. In this paper, a framework consisting of a digital twin and
reinforcement learning agents is present to handle the issue. Specifically, we
propose to use the historical data and the neural networks to build a digital
twin model to simulate the state variation law of the real environment. Then,
we use the data generated by the network slicing environment to calibrate the
digital twin so that it is in sync with the real environment. Finally, DRL for
slice optimization optimizes its own performance in this virtual
pre-verification environment. We conducted an exhaustive verification of the
proposed digital twin framework to confirm its scalability. Specifically, we
propose to use loss landscapes to visualize the generalization of DRL
solutions. We explore a distillation-based optimization scheme for lightweight
slicing strategies. In addition, we also extend the framework to offline
reinforcement learning, where solutions can be used to obtain intelligent
decisions based solely on historical data. Numerical simulation experiments
show that the proposed digital twin can significantly improve the performance
of the slice optimization strategy
- …