Jarred Barber - Academia.edu (original) (raw)

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Papers by Jarred Barber

We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot... more We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-distance-based soft k-means clustering procedure with a modified state of the art neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data. We evaluate our method on transductive few-shot learning tasks, in which the goal is to jointly predict labels for query (test) examples given a set of support (training) examples. We achieve state of the art performance on the Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks. All trained models and code have been made publicly available1.

ArXiv, 2020

We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot... more We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-distance-based soft k-means clustering procedure with a modified state of the art neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data. We evaluate our method on transductive few-shot learning tasks, in which the goal is to jointly predict labels for query (test) examples given a set of support (training) examples. We achieve new state of the art performance on Meta-Dataset, and produce competitive results on mini- and tiered-ImageNet benchmarks.

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

In recent years, convolutional neural networks (CNNs) have been successfully applied for automati... more In recent years, convolutional neural networks (CNNs) have been successfully applied for automatic target recognition (ATR) in synthetic aperture radar (SAR) data. However, it is challenging to train a CNN with high classification accuracy when labeled data is limited. This is often the case with SAR ATR in practice, because collecting large amounts of labeled SAR data is both difficult and expensive. Using a simulator to generate SAR images offers a possible solution. Unfortunately, CNNs trained on simulated data may not be directly transferable to real data. In this paper, we introduce a method to refine simulated SAR data based on deep residual networks. We learn a refinement function from simulated to real SAR data through a residual learning framework, and use the function to refine simulated images. Using the MSTAR dataset, we demonstrate that a CNN-based SAR ATR system trained on simulated data under residual network refinements can yield much higher classification accuracy a...

ArXiv, 2020

Gaussian processes are powerful models for probabilistic machine learning, but are limited in app... more Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their O(N3)O(N^3)O(N3) inference complexity. We propose a method for deriving parametric families of kernel functions with compact spatial support, which yield naturally sparse kernel matrices and enable fast Gaussian process inference via sparse linear algebra. These families generalize known compactly-supported kernel functions, such as the Wendland polynomials. The parameters of this family of kernels can be learned from data using maximum likelihood estimation. Alternatively, we can quickly compute compact approximations of a target kernel using convex optimization. We demonstrate that these approximations incur minimal error over the exact models when modeling data drawn directly from a target GP, and can out-perform the traditional GP kernels on real-world signal reconstruction tasks, while exhibiting sub-quadratic inference complexity.

IEEE Geoscience and Remote Sensing Letters, 2015

2013 IEEE Radar Conference (RadarCon13), 2013

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012

We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot... more We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-distance-based soft k-means clustering procedure with a modified state of the art neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data. We evaluate our method on transductive few-shot learning tasks, in which the goal is to jointly predict labels for query (test) examples given a set of support (training) examples. We achieve state of the art performance on the Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks. All trained models and code have been made publicly available1.

ArXiv, 2020

We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot... more We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-distance-based soft k-means clustering procedure with a modified state of the art neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data. We evaluate our method on transductive few-shot learning tasks, in which the goal is to jointly predict labels for query (test) examples given a set of support (training) examples. We achieve new state of the art performance on Meta-Dataset, and produce competitive results on mini- and tiered-ImageNet benchmarks.

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

In recent years, convolutional neural networks (CNNs) have been successfully applied for automati... more In recent years, convolutional neural networks (CNNs) have been successfully applied for automatic target recognition (ATR) in synthetic aperture radar (SAR) data. However, it is challenging to train a CNN with high classification accuracy when labeled data is limited. This is often the case with SAR ATR in practice, because collecting large amounts of labeled SAR data is both difficult and expensive. Using a simulator to generate SAR images offers a possible solution. Unfortunately, CNNs trained on simulated data may not be directly transferable to real data. In this paper, we introduce a method to refine simulated SAR data based on deep residual networks. We learn a refinement function from simulated to real SAR data through a residual learning framework, and use the function to refine simulated images. Using the MSTAR dataset, we demonstrate that a CNN-based SAR ATR system trained on simulated data under residual network refinements can yield much higher classification accuracy a...

ArXiv, 2020

Gaussian processes are powerful models for probabilistic machine learning, but are limited in app... more Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their O(N3)O(N^3)O(N3) inference complexity. We propose a method for deriving parametric families of kernel functions with compact spatial support, which yield naturally sparse kernel matrices and enable fast Gaussian process inference via sparse linear algebra. These families generalize known compactly-supported kernel functions, such as the Wendland polynomials. The parameters of this family of kernels can be learned from data using maximum likelihood estimation. Alternatively, we can quickly compute compact approximations of a target kernel using convex optimization. We demonstrate that these approximations incur minimal error over the exact models when modeling data drawn directly from a target GP, and can out-perform the traditional GP kernels on real-world signal reconstruction tasks, while exhibiting sub-quadratic inference complexity.

IEEE Geoscience and Remote Sensing Letters, 2015

2013 IEEE Radar Conference (RadarCon13), 2013

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012

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