621 research outputs found
Anti-Aliasing Add-On for Deep Prior Seismic Data Interpolation
Data interpolation is a fundamental step in any seismic processing workflow.
Among machine learning techniques recently proposed to solve data interpolation
as an inverse problem, Deep Prior paradigm aims at employing a convolutional
neural network to capture priors on the data in order to regularize the
inversion. However, this technique lacks of reconstruction precision when
interpolating highly decimated data due to the presence of aliasing. In this
work, we propose to improve Deep Prior inversion by adding a directional
Laplacian as regularization term to the problem. This regularizer drives the
optimization towards solutions that honor the slopes estimated from the
interpolated data low frequencies. We provide some numerical examples to
showcase the methodology devised in this manuscript, showing that our results
are less prone to aliasing also in presence of noisy and corrupted data
Deep Prior-Based Audio Inpainting Using Multi-Resolution Harmonic Convolutional Neural Networks
In this manuscript, we propose a novel method to perform audio inpainting, i.e., the restoration of audio signals presenting multiple missing parts. Audio inpainting can be interpreted in the context of inverse problems as the task of reconstructing an audio signal from its corrupted observation. For this reason, our method is based on a deep prior approach, a recently proposed technique that proved to be effective in the solution of many inverse problems, among which image inpainting. Deep prior allows one to consider the structure of a neural network as an implicit prior and to adopt it as a regularizer. Differently from the classical deep learning paradigm, deep prior performs a single-element training and thus it can be applied to corrupted audio signals independently from the available training data sets. In the context of audio inpainting, a network presenting relevant audio priors will possibly generate a restored version of an audio signal, only provided with its corrupted observation. Our method exploits a time-frequency representation of audio signals and makes use of a multi-resolution convolutional autoencoder, that has been enhanced to perform the harmonic convolution operation. Results show that the proposed technique is able to provide a coherent and meaningful reconstruction of the corrupted audio. It is also able to outperform the methods considered for comparison, in its domain of application
Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks
It is difficult to precisely annotate object instances and their semantics in
3D space, and as such, synthetic data are extensively used for these tasks,
e.g., category-level 6D object pose and size estimation. However, the easy
annotations in synthetic domains bring the downside effect of synthetic-to-real
(Sim2Real) domain gap. In this work, we aim to address this issue in the task
setting of Sim2Real, unsupervised domain adaptation for category-level 6D
object pose and size estimation. We propose a method that is built upon a novel
Deep Prior Deformation Network, shortened as DPDN. DPDN learns to deform
features of categorical shape priors to match those of object observations, and
is thus able to establish deep correspondence in the feature space for direct
regression of object poses and sizes. To reduce the Sim2Real domain gap, we
formulate a novel self-supervised objective upon DPDN via consistency learning;
more specifically, we apply two rigid transformations to each object
observation in parallel, and feed them into DPDN respectively to yield dual
sets of predictions; on top of the parallel learning, an inter-consistency term
is employed to keep cross consistency between dual predictions for improving
the sensitivity of DPDN to pose changes, while individual intra-consistency
ones are used to enforce self-adaptation within each learning itself. We train
DPDN on both training sets of the synthetic CAMERA25 and real-world REAL275
datasets; our results outperform the existing methods on REAL275 test set under
both the unsupervised and supervised settings. Ablation studies also verify the
efficacy of our designs. Our code is released publicly at
https://github.com/JiehongLin/Self-DPDN.Comment: Accepted by ECCV202
- …