176 research outputs found
Placing objects in context via inpainting for out-of-distribution segmentation
When deploying a semantic segmentation model into the real world, it will inevitably be confronted
with semantic classes unseen during training. Thus, to safely deploy such systems, it is crucial to
accurately evaluate and improve their anomaly segmentation capabilities. However, acquiring and
labelling semantic segmentation data is expensive and unanticipated conditions are long-tail and
potentially hazardous. Indeed, existing anomaly segmentation datasets capture a limited number of
anomalies, lack realism or have strong domain shifts. In this paper, we propose the Placing Objects
in Context (POC) pipeline to realistically add any object into any image via diffusion models. POC
can be used to easily extend any dataset with an arbitrary number of objects. In our experiments,
we present different anomaly segmentation datasets based on POC-generated data and show that
POC can improve the performance of recent state-of-the-art anomaly fine-tuning methods in several
standardized benchmarks. POC is also effective to learn new classes. For example, we use it to edit
Cityscapes samples by adding a subset of Pascal classes and show that models trained on such data
achieve comparable performance to the Pascal-trained baseline. This corroborates the low sim-to-real
gap of models trained on POC-generated images
Progressive Skeletonization: Trimming more fat from a network at initialization
Recent studies have shown that skeletonization (pruning parameters) of
networks \textit{at initialization} provides all the practical benefits of
sparsity both at inference and training time, while only marginally degrading
their performance. However, we observe that beyond a certain level of sparsity
(approx ), these approaches fail to preserve the network performance, and
to our surprise, in many cases perform even worse than trivial random pruning.
To this end, we propose an objective to find a skeletonized network with
maximum {\em foresight connection sensitivity} (FORCE) whereby the
trainability, in terms of connection sensitivity, of a pruned network is taken
into consideration. We then propose two approximate procedures to maximize our
objective (1) Iterative SNIP: allows parameters that were unimportant at
earlier stages of skeletonization to become important at later stages; and (2)
FORCE: iterative process that allows exploration by allowing already pruned
parameters to resurrect at later stages of skeletonization. Empirical analyses
on a large suite of experiments show that our approach, while providing at
least as good a performance as other recent approaches on moderate pruning
levels, provides remarkably improved performance on higher pruning levels
(could remove up to parameters while keeping the networks trainable).
Code can be found in https://github.com/naver/force
Catastrophic overfitting can be induced with discriminative non-robust features
Adversarial training (AT) is the de facto method for building robust neural
networks, but it can be computationally expensive. To mitigate this, fast
single-step attacks can be used, but this may lead to catastrophic overfitting
(CO). This phenomenon appears when networks gain non-trivial robustness during
the first stages of AT, but then reach a breaking point where they become
vulnerable in just a few iterations. The mechanisms that lead to this failure
mode are still poorly understood. In this work, we study the onset of CO in
single-step AT methods through controlled modifications of typical datasets of
natural images. In particular, we show that CO can be induced at much smaller
values than it was observed before just by injecting images with
seemingly innocuous features. These features aid non-robust classification but
are not enough to achieve robustness on their own. Through extensive
experiments we analyze this novel phenomenon and discover that the presence of
these easy features induces a learning shortcut that leads to CO. Our findings
provide new insights into the mechanisms of CO and improve our understanding of
the dynamics of AT. The code to reproduce our experiments can be found at
https://github.com/gortizji/co_features.Comment: Published in Transactions on Machine Learning Research (TMLR
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
The emergence of Deep Neural Networks (DNNs) has revolutionized various
domains, enabling the resolution of complex tasks spanning image recognition,
natural language processing, and scientific problem-solving. However, this
progress has also exposed a concerning vulnerability: adversarial examples.
These crafted inputs, imperceptible to humans, can manipulate machine learning
models into making erroneous predictions, raising concerns for safety-critical
applications. An intriguing property of this phenomenon is the transferability
of adversarial examples, where perturbations crafted for one model can deceive
another, often with a different architecture. This intriguing property enables
"black-box" attacks, circumventing the need for detailed knowledge of the
target model. This survey explores the landscape of the adversarial
transferability of adversarial examples. We categorize existing methodologies
to enhance adversarial transferability and discuss the fundamental principles
guiding each approach. While the predominant body of research primarily
concentrates on image classification, we also extend our discussion to
encompass other vision tasks and beyond. Challenges and future prospects are
discussed, highlighting the importance of fortifying DNNs against adversarial
vulnerabilities in an evolving landscape
MLLP-VRAIN Spanish ASR Systems for the AlbayzĂn-RTVE 2020 Speech-to-Text Challenge: Extension
[EN] This paper describes the automatic speech recognition (ASR) systems built by the MLLP-VRAIN research group of Universitat Politècnica de València for the AlbayzĂn-RTVE 2020 Speech-to-Text Challenge, and includes an extension of the work consisting of building and evaluating equivalent systems under the closed data conditions from the 2018 challenge. The primary system (p-streaming_1500ms_nlt) was a hybrid ASR system using streaming one-pass decoding with a context window of 1.5 seconds. This system achieved 16.0% WER on the test-2020 set. We also submitted three contrastive systems. From these, we highlight the system c2-streaming_600ms_t which, following a similar configuration as the primary system with a smaller context window of 0.6 s, scored 16.9% WER points on the same test set, with a measured empirical latency of 0.81 ± 0.09 s (mean ± stdev). That is, we obtained state-of-the-art latencies for high-quality automatic live captioning with a small WER degradation of 6% relative. As an extension, the equivalent closed-condition systems obtained 23.3% WER and 23.5% WER, respectively. When evaluated with an unconstrained language model, we obtained 19.9% WER and 20.4% WER; i.e., not far behind the top-performing systems with only 5% of the full acoustic data and with the extra ability of being streaming-capable. Indeed, all of these streaming systems could be put into production environments for automatic captioning of live media streams.The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements no. 761758 (X5Gon) and 952215 (TAILOR), and Erasmus+ Education programme under grant agreement no. 20-226-093604-SCH (EXPERT); the Government of Spain's grant RTI2018-094879-B-I00 (Multisub) funded by MCIN/AEI/10.13039/501100011033 & "ERDF A way of making Europe", and FPU scholarships FPU14/03981 and FPU18/04135; the Generalitat Valenciana's research project Classroom Activity Recognition (ref. PROMETEO/2019/111), and predoctoral research scholarship ACIF/2017/055; and the Universitat Politecnica de Valencia's PAID-01-17 R&D support programme.Baquero-Arnal, P.; Jorge-Cano, J.; GimĂ©nez Pastor, A.; Iranzo-Sánchez, J.; PĂ©rez-González De Martos, AM.; GarcĂ©s DĂaz-MunĂo, G.; Silvestre CerdĂ , JA.... (2022). MLLP-VRAIN Spanish ASR Systems for the AlbayzĂn-RTVE 2020 Speech-to-Text Challenge: Extension. Applied Sciences. 12(2):1-14. https://doi.org/10.3390/app1202080411412
Cellular distribution of the histamine H3 receptor in the basal ganglia : functional modulation of dopamine and glutamate neurotransmission
This is the author's version of a work that was accepted for publication in Basal ganglia. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Vol. 3 NĂşm. 2 (Jul. 2013)Altres ajuts: Red_de_Trastornos_Adictivos/RD06/0001/0015Histamine H3 receptors (H3R) are widely expressed in the brain where they participate in sleep-wake cycle and cognition among other functions. Despite their high expression in some regions of the basal ganglia, their functional role in this forebrain neural network remains unclear. The present findings provide in situ hybridization and immunohistochemical evidence for H3R expression in several neuronal populations of the rat basal ganglia but not in astrocytes (glial fibrillary acidic protein immunoreactive cells). We demonstrate the presence of H3R mRNA and protein in dopaminergic neurons (tyrosine hydroxylase positive) of the ventral tegmental area and substantia nigra. In the dorsal and ventral (nucleus accumbens) striatal complex we show H3R immunoreactivity in cholinergic (choline acetyltransferase immunoreactive) and GABAergic neurons (substance P, proenkephalin or dopamine D1 receptor positive) as well as in corticostriatal terminals (VGLUT1-immunoreactive). Double-labelling experiments in the medial prefrontal cortex show that H3R is expressed in D1R-positive interneurons and VGLUT1-positive corticostriatal output neurons. Our functional experiments confirm that H3R ligands modulate dopamine synthesis and the probability of glutamate release in the striatum from cortico-striatal afferents. The presence of H3R in such different neuronal populations and its involvement in the control of striatal dopaminergic and glutamatergic transmission ascribes a complex role to H3R in the function of the basal ganglia neural network
The PAU Survey: A Forward Modeling Approach for Narrow-band Imaging
Weak gravitational lensing is a powerful probe of the dark sector, once
measurement systematic errors can be controlled. In Refregier & Amara (2014), a
calibration method based on forward modeling, called MCCL, was proposed. This
relies on fast image simulations (e.g., UFig; Berge et al. 2013) that capture
the key features of galaxy populations and measurement effects. The MCCL
approach has been used in Herbel et al. (2017) to determine the redshift
distribution of cosmological galaxy samples and, in the process, the authors
derived a model for the galaxy population mainly based on broad-band
photometry. Here, we test this model by forward modeling the 40 narrow-band
photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the
same forced photometric pipeline on data and simulations using Source Extractor
(Bertin & Arnouts 1996). The image simulation scheme performance is assessed at
the image and at the catalogues level. We find good agreement for the
distribution of pixel values, the magnitudes, in the magnitude-size relation
and the interband correlations. A principal component analysis is then
performed, in order to derive a global comparison of the narrow-band photometry
between the data and the simulations. We use a `mixing' matrix to quantify the
agreement between the observed and simulated sets of Principal Components
(PCs). We find good agreement, especially for the first three most significant
PCs. We also compare the coefficients of the PCs decomposition. While there are
slight differences for some coefficients, we find that the distributions are in
good agreement. Together, our results show that the galaxy population model
derived from broad-band photometry is in good overall agreement with the PAUS
data. This offers good prospect for incorporating spectral information to the
galaxy model by adjusting it to the PAUS narrow-band data using forward
modeling.Comment: Submitted to JCAP, 28 pages, 15 figures, 3 appendice
Natural history of patients with venous thromboembolism and hereditary hemorrhagic telangiectasia. Findings from the RIETE registry
Background: Limited data exist about the clinical presentation, ideal therapy and outcomes of patients with hereditary hemorrhagic telangiectasia (HHT) who develop venous thromboembolism (VTE). Methods: We used the data in the RIETE Registry to assess the clinical characteristics, therapeutic approaches and clinical outcomes during the course of anticoagulant therapy in patients with HHT according to initial presentation as pulmonary embolism (PE) or deep venous thrombosis (DVT). Results: Of 51,375 patients with acute VTE enrolled in RIETE from February 2009 to January 2019, 23 (0.04%) had HHT: 14 (61%) initially presented with PE and 9 (39%) with DVT alone. Almost half (47.8%) of the patients with VTE had a risk factor for VTE. Most PE and DVT patients received low-molecular-weight heparin for initial (71 and 100%, respectively) and long-term therapy (54 and 67%, respectively). During anticoagulation for VTE, the rate of bleeding events (major 2, non-major 6) far outweighed the rate of VTE recurrences (recurrent DVT 1): 50.1 bleeds per 100 patient-years (95%CI: 21.6-98.7) vs. 6.26 recurrences (95%CI: 0.31-30.9; p = 0.020). One major and three non-major bleeding were epistaxis. No patient died of bleeding. One patient died shortly after being diagnosed with acute PE. Conclusions: During anticoagulation for VTE in HHT patients, there were more bleeding events than VTE recurrences. Most bleeding episodes were non-major epistaxis
The PAU survey: Estimating galaxy photometry with deep learning
With the dramatic rise in high-quality galaxy data expected from Euclid and
Vera C. Rubin Observatory, there will be increasing demand for fast
high-precision methods for measuring galaxy fluxes. These will be essential for
inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a
deep learning method to measure photometry from galaxy images. Lumos builds on
BKGnet, an algorithm to predict the background and its associated error, and
predicts the background-subtracted flux probability density function. We have
developed Lumos for data from the Physics of the Accelerating Universe Survey
(PAUS), an imaging survey using 40 narrow-band filter camera (PAUCam). PAUCam
images are affected by scattered light, displaying a background noise pattern
that can be predicted and corrected for. On average, Lumos increases the SNR of
the observations by a factor of 2 compared to an aperture photometry algorithm.
It also incorporates other advantages like robustness towards distorting
artifacts, e.g. cosmic rays or scattered light, the ability of deblending and
less sensitivity to uncertainties in the galaxy profile parameters used to
infer the photometry. Indeed, the number of flagged photometry outlier
observations is reduced from 10% to 2%, comparing to aperture photometry.
Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with
the Deepz machine learning photo-z code and the photo-z outlier rate by 20%.
The photo-z improvement is lower than expected from the SNR increment, however
currently the photometric calibration and outliers in the photometry seem to be
its limiting factor.Comment: 20 pages, 22 figure
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