13 research outputs found
Synthetic Data for Model Selection
Recent breakthroughs in synthetic data generation approaches made it possible
to produce highly photorealistic images which are hardly distinguishable from
real ones. Furthermore, synthetic generation pipelines have the potential to
generate an unlimited number of images. The combination of high photorealism
and scale turn synthetic data into a promising candidate for improving various
machine learning (ML) pipelines. Thus far, a large body of research in this
field has focused on using synthetic images for training, by augmenting and
enlarging training data. In contrast to using synthetic data for training, in
this work we explore whether synthetic data can be beneficial for model
selection. Considering the task of image classification, we demonstrate that
when data is scarce, synthetic data can be used to replace the held out
validation set, thus allowing to train on a larger dataset. We also introduce a
novel method to calibrate the synthetic error estimation to fit that of the
real domain. We show that such calibration significantly improves the
usefulness of synthetic data for model selection
Asymmetric Image Retrieval with Cross Model Compatible Ensembles
The asymmetrical retrieval setting is a well suited solution for resource
constrained applications such as face recognition and image retrieval. In this
setting, a large model is used for indexing the gallery while a lightweight
model is used for querying. The key principle in such systems is ensuring that
both models share the same embedding space. Most methods in this domain are
based on knowledge distillation. While useful, they suffer from several
drawbacks: they are upper-bounded by the performance of the single best model
found and cannot be extended to use an ensemble of models in a straightforward
manner. In this paper we present an approach that does not rely on knowledge
distillation, rather it utilizes embedding transformation models. This allows
the use of N independently trained and diverse gallery models (e.g., trained on
different datasets or having a different architecture) and a single query
model. As a result, we improve the overall accuracy beyond that of any single
model while maintaining a low computational budget for querying. Additionally,
we propose a gallery image rejection method that utilizes the diversity between
multiple transformed embeddings to estimate the uncertainty of gallery images
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
Training fingerprint recognition models using synthetic data has recently
gained increased attention in the biometric community as it alleviates the
dependency on sensitive personal data. Existing approaches for fingerprint
generation are limited in their ability to generate diverse impressions of the
same finger, a key property for providing effective data for training
recognition models. To address this gap, we present FPGAN-Control, an identity
preserving image generation framework which enables control over the
fingerprint's image appearance (e.g., fingerprint type, acquisition device,
pressure level) of generated fingerprints. We introduce a novel appearance loss
that encourages disentanglement between the fingerprint's identity and
appearance properties. In our experiments, we used the publicly available NIST
SD302 (N2N) dataset for training the FPGAN-Control model. We demonstrate the
merits of FPGAN-Control, both quantitatively and qualitatively, in terms of
identity preservation level, degree of appearance control, and low
synthetic-to-real domain gap. Finally, training recognition models using only
synthetic datasets generated by FPGAN-Control lead to recognition accuracies
that are on par or even surpass models trained using real data. To the best of
our knowledge, this is the first work to demonstrate this
Portaria 117
Revoga a Portaria nº092/CED/2016 e designa a professora Alessandra Mara Rotta de Oliveira para exercer a função de Coordenadora do Núcleo de Pesquisa da Educação na Pequena Infância (NUPEIN/CED)
Additional file 2: of Reduced changes in protein compared to mRNA levels across non-proliferating tissues
Supplementary Data. Supplementary table legends, figures, methods and results. (PDF 2108Â kb