91 research outputs found
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces
In this paper, we formalize the idea behind capsule nets of using a capsule
vector rather than a neuron activation to predict the label of samples. To this
end, we propose to learn a group of capsule subspaces onto which an input
feature vector is projected. Then the lengths of resultant capsules are used to
score the probability of belonging to different classes. We train such a
Capsule Projection Network (CapProNet) by learning an orthogonal projection
matrix for each capsule subspace, and show that each capsule subspace is
updated until it contains input feature vectors corresponding to the associated
class. We will also show that the capsule projection can be viewed as
normalizing the multiple columns of the weight matrix simultaneously to form an
orthogonal basis, which makes it more effective in incorporating novel
components of input features to update capsule representations. In other words,
the capsule projection can be viewed as a multi-dimensional weight
normalization in capsule subspaces, where the conventional weight normalization
is simply a special case of the capsule projection onto 1D lines. Only a small
negligible computing overhead is incurred to train the network in
low-dimensional capsule subspaces or through an alternative hyper-power
iteration to estimate the normalization matrix. Experiment results on image
datasets show the presented model can greatly improve the performance of the
state-of-the-art ResNet backbones by and that of the Densenet by
respectively at the same level of computing and memory expenses. The
CapProNet establishes the competitive state-of-the-art performance for the
family of capsule nets by significantly reducing test errors on the benchmark
datasets.Comment: Liheng Zhang, Marzieh Edraki, Guo-Jun Qi. CapProNet: Deep Feature
Learning via Orthogonal Projections onto Capsule Subspaces, in Proccedings of
Thirty-second Conference on Neural Information Processing Systems (NIPS
2018), Palais des Congr\`es de Montr\'eal, Montr\'eal, Canda, December 3-8,
201
Motion-corrected Fourier ptychography
Fourier ptychography (FP) is a recently proposed computational imaging
technique for high space-bandwidth product imaging. In real setups such as
endoscope and transmission electron microscope, the common sample motion
largely degrades the FP reconstruction and limits its practicability. In this
paper, we propose a novel FP reconstruction method to efficiently correct for
unknown sample motion. Specifically, we adaptively update the sample's Fourier
spectrum from low spatial-frequency regions towards high spatial-frequency
ones, with an additional motion recovery and phase-offset compensation
procedure for each sub-spectrum. Benefiting from the phase retrieval redundancy
theory, the required large overlap between adjacent sub-spectra offers an
accurate guide for successful motion recovery. Experimental results on both
simulated data and real captured data show that the proposed method can correct
for unknown sample motion with its standard deviation being up to 10% of the
field-of-view scale. We have released our source code for non-commercial use,
and it may find wide applications in related FP platforms such as endoscopy and
transmission electron microscopy
Global versus Localized Generative Adversarial Nets
In this paper, we present a novel localized Generative Adversarial Net (GAN)
to learn on the manifold of real data. Compared with the classic GAN that {\em
globally} parameterizes a manifold, the Localized GAN (LGAN) uses local
coordinate charts to parameterize distinct local geometry of how data points
can transform at different locations on the manifold. Specifically, around each
point there exists a {\em local} generator that can produce data following
diverse patterns of transformations on the manifold. The locality nature of
LGAN enables local generators to adapt to and directly access the local
geometry without need to invert the generator in a global GAN. Furthermore, it
can prevent the manifold from being locally collapsed to a dimensionally
deficient tangent subspace by imposing an orthonormality prior between
tangents. This provides a geometric approach to alleviating mode collapse at
least locally on the manifold by imposing independence between data
transformations in different tangent directions. We will also demonstrate the
LGAN can be applied to train a robust classifier that prefers locally
consistent classification decisions on the manifold, and the resultant
regularizer is closely related with the Laplace-Beltrami operator. Our
experiments show that the proposed LGANs can not only produce diverse image
transformations, but also deliver superior classification performances
Autotoxin affects the rhizosphere microbial community structure by influencing the secretory characteristics of grapevine roots
Autotoxins secreted by roots into the soil can trigger rhizosphere microecological imbalances and affect root secretory properties resulting in conditions such as replanting disease. However, information on the effect of autotoxins on root secretion characteristics and regulation of the composition of rhizosphere microorganisms by altered root exudates is limited. In this study, autotoxin ρ-hydroxybenzoic acid (4-HBA) was added to the soil of potted grapevine seedlings, CO2 pulse-labeling, and DNA stable isotope probing were used to track the rhizosphere microbiome that assimilates root exudates. Bacterial and fungal microbiomes that assimilated plant-derived carbon were identified by high-throughput sequencing. Results showed that 4-HBA treatment altered bacterial and fungal communities in 13C-labeled organisms, with a lower abundance of beneficial bacteria (e.g., Gemmatimonas, Streptomyces, and Bacillus) and a higher abundance of potential pathogen fungi (e.g., Fusarium, Neocosmospora, Gibberella, and Fusicolla) by changing the composition of root exudates. The exogenous addition of upregulated compound mixtures of root exudates reduced the abundance of beneficial bacterial Bacillus and increased the abundance of potential pathogen fungi Gibberella. These results suggest that 4-HBA can alter root secretion properties and altered root exudates may enrich certain potential pathogens and reduce certain beneficial bacteria, thereby unbalancing the structure of the rhizosphere microbial community
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