6 research outputs found
Domain Generalization by Rejecting Extreme Augmentations
Data augmentation is one of the most effective techniques for regularizing
deep learning models and improving their recognition performance in a variety
of tasks and domains. However, this holds for standard in-domain settings, in
which the training and test data follow the same distribution. For the
out-of-domain case, where the test data follow a different and unknown
distribution, the best recipe for data augmentation is unclear. In this paper,
we show that for out-of-domain and domain generalization settings, data
augmentation can provide a conspicuous and robust improvement in performance.
To do that, we propose a simple training procedure: (i) use uniform sampling on
standard data augmentation transformations; (ii) increase the strength
transformations to account for the higher data variance expected when working
out-of-domain, and (iii) devise a new reward function to reject extreme
transformations that can harm the training. With this procedure, our data
augmentation scheme achieves a level of accuracy that is comparable to or
better than state-of-the-art methods on benchmark domain generalization
datasets. Code: \url{https://github.com/Masseeh/DCAug
Recommended from our members
Miscarriage risk assessment: a bioinformatic approach to identifying candidate lethal genes and variants
PurposeMiscarriage, often resulting from a variety of genetic factors, is a common pregnancy outcome. Preconception genetic carrier screening (PGCS) identifies at-risk partners for newborn genetic disorders; however, PGCS panels currently lack miscarriage-related genes. In this study, we evaluated the potential impact of both known and candidate genes on prenatal lethality and the effectiveness of PGCS in diverse populations.MethodsWe analyzed 125,748 human exome sequences and mouse and human gene function databases. Our goals were to identify genes crucial for human fetal survival (lethal genes), to find variants not present in a homozygous state in healthy humans, and to estimate carrier rates of known and candidate lethal genes in various populations and ethnic groups.ResultsThis study identified 138 genes in which heterozygous lethal variants are present in the general population with a frequency of 0.5% or greater. Screening for these 138 genes could identify 4.6% (in the Finnish population) to 39.8% (in the East Asian population) of couples at risk of miscarriage. This explains the cause of pregnancy loss in approximately 1.1-10% of cases affected by biallelic lethal variants.ConclusionThis study has identified a set of genes and variants potentially associated with lethality across different ethnic backgrounds. The variation of these genes across ethnic groups underscores the need for a comprehensive, pan-ethnic PGCS panel that includes genes related to miscarriage