525 research outputs found
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the long-lasting and sole standard for
comparing the performance of different image classification models, including
the ImageNet competition. However, recent studies have highlighted the lack of
robustness in well-trained deep neural networks to adversarial examples.
Visually imperceptible perturbations to natural images can easily be crafted
and mislead the image classifiers towards misclassification. To demystify the
trade-offs between robustness and accuracy, in this paper we thoroughly
benchmark 18 ImageNet models using multiple robustness metrics, including the
distortion, success rate and transferability of adversarial examples between
306 pairs of models. Our extensive experimental results reveal several new
insights: (1) linear scaling law - the empirical and
distortion metrics scale linearly with the logarithm of classification error;
(2) model architecture is a more critical factor to robustness than model size,
and the disclosed accuracy-robustness Pareto frontier can be used as an
evaluation criterion for ImageNet model designers; (3) for a similar network
architecture, increasing network depth slightly improves robustness in
distortion; (4) there exist models (in VGG family) that exhibit
high adversarial transferability, while most adversarial examples crafted from
one model can only be transferred within the same family. Experiment code is
publicly available at \url{https://github.com/huanzhang12/Adversarial_Survey}.Comment: Accepted by the European Conference on Computer Vision (ECCV) 201
Bilateral-Fuser: A Novel Multi-cue Fusion Architecture with Anatomical-aware Tokens for Fovea Localization
Accurate localization of fovea is one of the primary steps in analyzing
retinal diseases since it helps prevent irreversible vision loss. Although
current deep learning-based methods achieve better performance than traditional
methods, there still remain challenges such as utilizing anatomical landmarks
insufficiently, sensitivity to diseased retinal images and various image
conditions. In this paper, we propose a novel transformer-based architecture
(Bilateral-Fuser) for multi-cue fusion. This architecture explicitly
incorporates long-range connections and global features using retina and vessel
distributions for robust fovea localization. We introduce a spatial attention
mechanism in the dual-stream encoder for extracting and fusing self-learned
anatomical information. This design focuses more on features distributed along
blood vessels and significantly decreases computational costs by reducing token
numbers. Our comprehensive experiments show that the proposed architecture
achieves state-of-the-art performance on two public and one large-scale private
datasets. We also present that the Bilateral-Fuser is more robust on both
normal and diseased retina images and has better generalization capacity in
cross-dataset experiments.Comment: This paper is prepared for IEEE TRANSACTIONS ON MEDICAL IMAGIN
Trade-Off Between Beamforming and Macro-Diversity Gains in Distributed mMIMO
Industry and academia have been working towards the evolution from
Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed
mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many
cells, each served by a single Base Station equipped with several antennas, the
whole coverage area is jointly covered by several Access Points (AP) equipped
with few or single antennas. Nevertheless, when choosing between deploying more
APs with few or single antennas or fewer APs equipped with many antennas, one
observes an inherent trade-off between the beamforming and macro-diversity
gains that has not been investigated in the literature. Given a total number of
antenna elements and total downlink power, under a channel model that takes
into account a probability of Line-of-Sight (LoS) as a function of the distance
between the User Equipments (UEs) and APs, our numerical results show that
there exists a ``sweet spot" on the optimal number of APs and of antenna
elements per AP which is a function of the physical dimensions of the coverage
area.Comment: 6 pages, 3 figures. Manuscript submitted to the IEEE Wireless
Communications and Networking Conference (WCNC) 2024, Dubai, United Arab
Emirate
Effect of saline stress on the physiology and growth of maize hybrids and their related inbred lines
Salinity is one major abiotic stress that restrict plant growth and crop productivity. In maize (Zea mays L), salt stress causes significant yield loss each year. However, indices of maize response to salt stress are not completely explored and a desired method for maize salt tolerance evaluation is still not established. A Chinese leading maize variety Jingke968 showed various resistance to environmental factors, including salt stress. To compare its salt tolerance to other superior maize varieties, we examined the physiological and growth responses of three important maize hybrids and their related inbred lines under the control and salt stress conditions. By compar- ing the physiological parameters under control and salt treatment, we demonstrated that different salt tolerance mechanisms may be involved in different genotypes, such as the elevation of superoxide dismutase activity and/ or proline content. With Principal Component Analysis of all the growth indicators in both germination and seedling stages, along with the germination rate, superoxide dismutase activity, proline content, malondialdehyde content, relative electrolyte leakage, we were able to show that salt resistance levels of hybrids and their related inbred lines were Jingke968 > Zhengdan958 > X1132 and X1132M > Jing724 > Chang7-2 > Zheng58 > X1132F, respectively, which was consistent with the saline field observation. Our results not only contribute to a better understanding of salt stress response in three important hybrids and their related inbred lines, but also this evaluation system might be applied for an accurate assessment of salt resistance in other germplasms and breeding material
Stepwise Feature Fusion: Local Guides Global
Colonoscopy, currently the most efficient and recognized colon polyp detection technology, is necessary for early screening and prevention of colorectal cancer. However, due to the varying size and complex morphological features of colonic polyps as well as the indistinct boundary between polyps and mucosa, accurate segmentation of polyps is still challenging. Deep learning has become popular for accurate polyp segmentation tasks with excellent results. However, due to the structure of polyps image and the varying shapes of polyps, it is easy for existing deep learning models to overfit the current dataset. As a result, the model may not process unseen colonoscopy data. To address this, we propose a new state-of-the-art model for medical image segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve the generalization ability of models. Specifically, our proposed Progressive Locality Decoder can be adapted to the pyramid Transformer backbone to emphasize local features and restrict attention dispersion. The SSFormer achieves state-of-the-art performance in both learning and generalization assessment
Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
Purpose: Congenital heart defect (CHD) is the most common birth defect.
Thoracic echocardiography (TTE) can provide sufficient cardiac structure
information, evaluate hemodynamics and cardiac function, and is an effective
method for atrial septal defect (ASD) examination. This paper aims to study a
deep learning method based on cardiac ultrasound video to assist in ASD
diagnosis. Materials and methods: We select two standard views of the atrial
septum (subAS) and low parasternal four-compartment view (LPS4C) as the two
views to identify ASD. We enlist data from 300 children patients as part of a
double-blind experiment for five-fold cross-validation to verify the
performance of our model. In addition, data from 30 children patients (15
positives and 15 negatives) are collected for clinician testing and compared to
our model test results (these 30 samples do not participate in model training).
We propose an echocardiography video-based atrial septal defect diagnosis
system. In our model, we present a block random selection, maximal agreement
decision and frame sampling strategy for training and testing respectively,
resNet18 and r3D networks are used to extract the frame features and aggregate
them to build a rich video-level representation. Results: We validate our model
using our private dataset by five-cross validation. For ASD detection, we
achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and
81.99 F1 score. Conclusion: The proposed model is multiple instances
learning-based deep learning model for video atrial septal defect detection
which effectively improves ASD detection accuracy when compared to the
performances of previous networks and clinical doctors
Ultra-small topological spin textures with size of 1.3nm at above room temperature in Fe78Si9B13 amorphous alloy
Topologically protected spin textures, such as skyrmions1,2 and vortices3,4,
are robust against perturbations, serving as the building blocks for a range of
topological devices5-9. In order to implement these topological devices, it is
necessary to find ultra-small topological spin textures at room temperature,
because small size implies the higher topological charge density, stronger
signal of topological transport10,11 and the higher memory density or
integration for topological quantum devices5-9. However, finding ultra-small
topological spin textures at high temperatures is still a great challenge up to
now. Here we find ultra-small topological spin textures in Fe78Si9B13 amorphous
alloy. We measured a large topological Hall effect (THE) up to above room
temperature, indicating the existence of highly densed and ultra-small
topological spin textures in the samples. Further measurements by small-angle
neutron scattering (SANS) reveal that the average size of ultra-small magnetic
texture is around 1.3nm. Our Monte Carlo simulations show that such ultra-small
spin texture is topologically equivalent to skyrmions, which originate from
competing frustration and Dzyaloshinskii-Moriya interaction12,13 coming from
amorphous structure14-17. Taking a single topological spin texture as one bit
and ignoring the distance between them, we evaluated the ideal memory density
of Fe78Si9B13, which reaches up to 4.44*104 gigabits (43.4 TB) per in2 and is 2
times of the value of GdRu2Si218 at 5K. More important, such high memory
density can be obtained at above room temperature, which is 4 orders of
magnitude larger than the value of other materials at the same temperature.
These findings provide a unique candidate for magnetic memory devices with
ultra-high density.Comment: 26 pages, 4 figure
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