198 research outputs found
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Deep neural networks are vulnerable to adversarial attacks. The literature is
rich with algorithms that can easily craft successful adversarial examples. In
contrast, the performance of defense techniques still lags behind. This paper
proposes ME-Net, a defense method that leverages matrix estimation (ME). In
ME-Net, images are preprocessed using two steps: first pixels are randomly
dropped from the image; then, the image is reconstructed using ME. We show that
this process destroys the adversarial structure of the noise, while
re-enforcing the global structure in the original image. Since humans typically
rely on such global structures in classifying images, the process makes the
network mode compatible with human perception. We conduct comprehensive
experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and
Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows
that ME-Net consistently outperforms prior techniques, improving robustness
against both black-box and white-box attacks.Comment: ICML 201
Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing
Given significant air pollution problems, air quality index (AQI) monitoring
has recently received increasing attention. In this paper, we design a mobile
AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS,
to efficiently build fine-grained AQI maps in realtime. Specifically, we first
propose the Gaussian plume model on basis of the neural network (GPM-NN), to
physically characterize the particle dispersion in the air. Based on GPM-NN, we
propose a battery efficient and adaptive monitoring algorithm to monitor AQI at
the selected locations and construct an accurate AQI map with the sensed data.
The proposed adaptive monitoring algorithm is evaluated in two typical
scenarios, a two-dimensional open space like a roadside park, and a
three-dimensional space like a courtyard inside a building. Experimental
results demonstrate that our system can provide higher prediction accuracy of
AQI with GPM-NN than other existing models, while greatly reducing the power
consumption with the adaptive monitoring algorithm
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
Real-world data often exhibit imbalanced label distributions. Existing
studies on data imbalance focus on single-domain settings, i.e., samples are
from the same data distribution. However, natural data can originate from
distinct domains, where a minority class in one domain could have abundant
instances from other domains. We formalize the task of Multi-Domain Long-Tailed
Recognition (MDLT), which learns from multi-domain imbalanced data, addresses
label imbalance, domain shift, and divergent label distributions across
domains, and generalizes to all domain-class pairs. We first develop the
domain-class transferability graph, and show that such transferability governs
the success of learning in MDLT. We then propose BoDA, a theoretically grounded
learning strategy that tracks the upper bound of transferability statistics,
and ensures balanced alignment and calibration across imbalanced domain-class
distributions. We curate five MDLT benchmarks based on widely-used multi-domain
datasets, and compare BoDA to twenty algorithms that span different learning
strategies. Extensive and rigorous experiments verify the superior performance
of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on
Domain Generalization benchmarks, highlighting the importance of addressing
data imbalance across domains, which can be crucial for improving
generalization to unseen domains. Code and data are available at:
https://github.com/YyzHarry/multi-domain-imbalance.Comment: ECCV 202
Change is Hard: A Closer Look at Subpopulation Shift
Machine learning models often perform poorly on subgroups that are
underrepresented in the training data. Yet, little is understood on the
variation in mechanisms that cause subpopulation shifts, and how algorithms
generalize across such diverse shifts at scale. In this work, we provide a
fine-grained analysis of subpopulation shift. We first propose a unified
framework that dissects and explains common shifts in subgroups. We then
establish a comprehensive benchmark of 20 state-of-the-art algorithms evaluated
on 12 real-world datasets in vision, language, and healthcare domains. With
results obtained from training over 10,000 models, we reveal intriguing
observations for future progress in this space. First, existing algorithms only
improve subgroup robustness over certain types of shifts but not others.
Moreover, while current algorithms rely on group-annotated validation data for
model selection, we find that a simple selection criterion based on worst-class
accuracy is surprisingly effective even without any group information. Finally,
unlike existing works that solely aim to improve worst-group accuracy (WGA), we
demonstrate the fundamental tradeoff between WGA and other important metrics,
highlighting the need to carefully choose testing metrics. Code and data are
available at: https://github.com/YyzHarry/SubpopBench.Comment: ICML 202
Supervised Contrastive Regression
Deep regression models typically learn in an end-to-end fashion and do not
explicitly try to learn a regression-aware representation. Their
representations tend to be fragmented and fail to capture the continuous nature
of regression tasks. In this paper, we propose Supervised Contrastive
Regression (SupCR), a framework that learns a regression-aware representation
by contrasting samples against each other based on their target distance. SupCR
is orthogonal to existing regression models, and can be used in combination
with such models to improve performance. Extensive experiments using five
real-world regression datasets that span computer vision, human-computer
interaction, and healthcare show that using SupCR achieves the state-of-the-art
performance and consistently improves prior regression baselines on all
datasets, tasks, and input modalities. SupCR also improves robustness to data
corruptions, resilience to reduced training data, performance on transfer
learning, and generalization to unseen targets.Comment: The first two authors contributed equally to this pape
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