82 research outputs found
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning
Neural network compression empowers the effective yet unwieldy deep
convolutional neural networks (CNN) to be deployed in resource-constrained
scenarios. Most state-of-the-art approaches prune the model in filter-level
according to the "importance" of filters. Despite their success, we notice they
suffer from at least two of the following problems: 1) The redundancy among
filters is not considered because the importance is evaluated independently. 2)
Cross-layer filter comparison is unachievable since the importance is defined
locally within each layer. Consequently, we must manually specify layer-wise
pruning ratios. 3) They are prone to generate sub-optimal solutions because
they neglect the inequality between reducing parameters and reducing
computational cost. Reducing the same number of parameters in different
positions in the network may reduce different computational cost. To address
the above problems, we develop a novel algorithm named as COP
(correlation-based pruning), which can detect the redundant filters
efficiently. We enable the cross-layer filter comparison through global
normalization. We add parameter-quantity and computational-cost regularization
terms to the importance, which enables the users to customize the compression
according to their preference (smaller or faster). Extensive experiments have
shown COP outperforms the others significantly. The code is released at
https://github.com/ZJULearning/COP.Comment: 7 pages, 4 figures, has been accepted by IJCAI201
SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition
Point cloud-based place recognition is crucial for mobile robots and
autonomous vehicles, especially when the global positioning sensor is not
accessible. LiDAR points are scattered on the surface of objects and buildings,
which have strong shape priors along different axes. To enhance message passing
along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed,
which is one of the main contributions in this paper. Comprehensive experiments
demonstrate that asymmetric convolution and its corresponding strategies
employed by SACB can contribute to the more effective representation of point
cloud feature. On this basis, Selective Feature Fusion Block (SFFB), which is
formed by stacking point- and channel-wise gating layers in a predefined
sequence, is proposed to selectively boost salient local features in certain
key regions, as well as to align the features before fusion phase. SACBs and
SFFBs are combined to construct a robust and accurate architecture for point
cloud-based place recognition, which is termed SelFLoc. Comparative
experimental results show that SelFLoc achieves the state-of-the-art (SOTA)
performance on the Oxford and other three in-house benchmarks with an
improvement of 1.6 absolute percentages on mean average recall@1
General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment
Compared to 2D images, 3D point clouds are much more sensitive to rotations.
We expect the point features describing certain patterns to keep invariant to
the rotation transformation. There are many recent SOTA works dedicated to
rotation-invariant learning for 3D point clouds. However, current
rotation-invariant methods lack generalizability on the point clouds in the
open scenes due to the reliance on the global distribution, \ie the global
scene and backgrounds. Considering that the output activation is a function of
the pattern and its orientation, we need to eliminate the effect of the
orientation.In this paper, inspired by the idea that the network weights can be
considered a set of points distributed in the same 3D space as the input
points, we propose Weight-Feature Alignment (WFA) to construct a local
Invariant Reference Frame (IRF) via aligning the features with the principal
axes of the network weights. Our WFA algorithm provides a general solution for
the point clouds of all scenes. WFA ensures the model achieves the target that
the response activity is a necessary and sufficient condition of the pattern
matching degree. Practically, we perform experiments on the point clouds of
both single objects and open large-range scenes. The results suggest that our
method almost bridges the gap between rotation invariance learning and normal
methods.Comment: 4 figure
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation
Unsupervised domain adaptation (UDA) methods facilitate the transfer of
models to target domains without labels. However, these methods necessitate a
labeled target validation set for hyper-parameter tuning and model selection.
In this paper, we aim to find an evaluation metric capable of assessing the
quality of a transferred model without access to target validation labels. We
begin with the metric based on mutual information of the model prediction.
Through empirical analysis, we identify three prevalent issues with this
metric: 1) It does not account for the source structure. 2) It can be easily
attacked. 3) It fails to detect negative transfer caused by the over-alignment
of source and target features. To address the first two issues, we incorporate
source accuracy into the metric and employ a new MLP classifier that is held
out during training, significantly improving the result. To tackle the final
issue, we integrate this enhanced metric with data augmentation, resulting in a
novel unsupervised UDA metric called the Augmentation Consistency Metric (ACM).
Additionally, we empirically demonstrate the shortcomings of previous
experiment settings and conduct large-scale experiments to validate the
effectiveness of our proposed metric. Furthermore, we employ our metric to
automatically search for the optimal hyper-parameter set, achieving superior
performance compared to manually tuned sets across four common benchmarks.
Codes will be available soon
MCS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders
Masked point modeling has become a promising scheme of self-supervised
pre-training for point clouds. Existing methods reconstruct either the original
points or related features as the objective of pre-training. However,
considering the diversity of downstream tasks, it is necessary for the model to
have both low- and high-level representation modeling capabilities to capture
geometric details and semantic contexts during pre-training. To this end,
MCS is proposed to enable the model with the above abilities. Specifically,
with masked point cloud as input, MCS introduces two decoders to predict
masked representations and the original points simultaneously. While an extra
decoder doubles parameters for the decoding process and may lead to
overfitting, we propose siamese decoders to keep the amount of learnable
parameters unchanged. Further, we propose an online codebook projecting
continuous tokens into discrete ones before reconstructing masked points. In
such way, we can enforce the decoder to take effect through the combinations of
tokens rather than remembering each token. Comprehensive experiments show that
MCS achieves superior performance at both classification and segmentation
tasks, outperforming existing methods
Berberine Inhibits Intestinal Polyps Growth in Apc (min/+) Mice via Regulation of Macrophage Polarization
Antitumor effect of berberine has been reported in a wide spectrum of cancer, however, the mechanisms of which are not fully understood. The aim of this study was to investigate the hypothesis that berberine suppresses tumorigenesis in the familial adenomatous polyposis (FAP) by regulating the macrophage polarization in Apc (min/+) mouse model. Berberine was given to Apc (min/+) mice for 12 weeks. Primary macrophages were isolated; after berberine treatment, the change in signaling cascade was determined. The total number and size of polyps were reduced remarkably in berberine group, compared with control group. A significant decrease in protein levels of F4/80, mannose receptor (MR), and COX-2 in stroma of intestinal polyps and an increase in the level of iNOS were observed after berberine treatment. The mRNA level of MR and Arg-1 in berberine group was significantly lower than those in IL-10 or IL-4 group, while no significant difference in mRNA levels of iNOS and CXCL10 was observed. The migration and invasiveness assays in vitro showed that berberine could reduce the capability of migration and invasiveness. These findings suggest that berberine attenuates intestinal tumorigenesis by inhibiting the migration and invasion of colorectal tumor cells via regulation of macrophage polarization
Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches
Accurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during 2001-2005. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37 +/- 180.84Tg (i.e., 532.02 +/- 99.71 g/m(2)) during 2001-2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo >DATsrc>MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo. (C) 2015 Elsevier Ltd. All rights reserved
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