534 research outputs found
Continual Contrastive Self-supervised Learning for Image Classification
For artificial learning systems, continual learning over time from a stream
of data is essential. The burgeoning studies on supervised continual learning
have achieved great progress, while the study of catastrophic forgetting in
unsupervised learning is still blank. Among unsupervised learning methods,
self-supervise learning method shows tremendous potential on visual
representation without any labeled data at scale. To improve the visual
representation of self-supervised learning, larger and more varied data is
needed. In the real world, unlabeled data is generated at all times. This
circumstance provides a huge advantage for the learning of the self-supervised
method. However, in the current paradigm, packing previous data and current
data together and training it again is a waste of time and resources. Thus, a
continual self-supervised learning method is badly needed. In this paper, we
make the first attempt to implement the continual contrastive self-supervised
learning by proposing a rehearsal method, which keeps a few exemplars from the
previous data. Instead of directly combining saved exemplars with the current
data set for training, we leverage self-supervised knowledge distillation to
transfer contrastive information among previous data to the current network by
mimicking similarity score distribution inferred by the old network over a set
of saved exemplars. Moreover, we build an extra sample queue to assist the
network to distinguish between previous and current data and prevent mutual
interference while learning their own feature representation. Experimental
results show that our method performs well on CIFAR100 and ImageNet-Sub.
Compared with the baselines, which learning tasks without taking any technique,
we improve the image classification top-1 accuracy by 1.60% on CIFAR100, 2.86%
on ImageNet-Sub and 1.29% on ImageNet-Full under 10 incremental steps setting
Nonlinear macromodel based on Krylov subspace for micromixer of the microfluidic chip
The simulation of MEMS (Micro-Electro-Mechanical-System) containing fluid field could not be well performed by conventional numerical analysis methods. The micro flow field characteristics can be simulated by using macromodel including a nonlinear analysis. This paper set up the macromodel of the micromixer of the microfluidic chip using Krylov subspace projection method. The system functions were assembled through finite element analysis using COMSOL. We took the flow field-concentration field analysis for micromixer finite element model. The finite element functions order is reduced by second-order Krylov subspace projection method based on Lanczos algorithm. It can be shown that the simulation results obtained by using the macromodel are highly consistent with the results of finite element analysis. The calculation using the macromodel is two orders of magnitude faster than the calculation performed by the finite element analysis method. This macromodel should facilitate the design of microfluidic devices with sophisticated channel networks
A note on the total chromatic number of Halin graphs with maximum degree 4
AbstractIn this paper, we prove that χT(G) = 5 for any Halin graph G with Δ(G) = 4, where Δ(G) and χT(G) denote the maximal degree and the total chromatic number of G, respectively
Spatial patterns of correlation between conspecific species and size diversity in forest ecosystems
Recently correlations between spatial species and size diversity have been found in many forest ecosystems around the world. They are likely to play a prominent role in nature's mechanisms of maintaining species and size diversity. In this study, we analysed the species population means of spatial species-mingling and sizeinequality indices in 36 large forest monitoring plots from the temperate and subtropical zones in China. Based on the literature we included eleven diversity-index combinations and considered their correlations for increasing numbers of nearest neighbours. Generally, positive correlations are related to between-species population size differences whilst negative correlations reflect within-species population size differences. Our results showed that the selected species-mingling and size-inequality indices produced different correlation patterns in one and the same monitoring site. We therefore defined a species-mingling size-inequality correlation space by computing the 0.025 and the 0.975 quantiles from the correlation data of the eleven index combinations. We noticed that each observed correlation space included 1-3 combinations of five basic geometric types and can be interpreted as the unique signature of a forest ecosystem in time. The correlation space allowed us to understand more clearly at which spatial scale within-species correlation was more influential than between-species inequality and vice versa. The shape of the correlation space is interpretable and gives important clues about the forest development stage of a forest ecosystem
CNS: Correspondence Encoded Neural Image Servo Policy
Image servo is an indispensable technique in robotic applications that helps
to achieve high precision positioning. The intermediate representation of image
servo policy is important to sensor input abstraction and policy output
guidance. Classical approaches achieve high precision but require clean
keypoint correspondence, and suffer from limited convergence basin or weak
feature error robustness. Recent learning-based methods achieve moderate
precision and large convergence basin on specific scenes but face issues when
generalizing to novel environments. In this paper, we encode keypoints and
correspondence into a graph and use graph neural network as architecture of
controller. This design utilizes both advantages: generalizable intermediate
representation from keypoint correspondence and strong modeling ability from
neural network. Other techniques including realistic data generation, feature
clustering and distance decoupling are proposed to further improve efficiency,
precision and generalization. Experiments in simulation and real-world verify
the effectiveness of our method in speed (maximum 40fps along with observer),
precision (<0.3{\deg} and sub-millimeter accuracy) and generalization
(sim-to-real without fine-tuning). Project homepage (full paper with
supplementary text, video and code): https://hhcaz.github.io/CNS-hom
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