64,870 research outputs found
Synthetic Sample Selection via Reinforcement Learning
Synthesizing realistic medical images provides a feasible solution to the
shortage of training data in deep learning based medical image recognition
systems. However, the quality control of synthetic images for data augmentation
purposes is under-investigated, and some of the generated images are not
realistic and may contain misleading features that distort data distribution
when mixed with real images. Thus, the effectiveness of those synthetic images
in medical image recognition systems cannot be guaranteed when they are being
added randomly without quality assurance. In this work, we propose a
reinforcement learning (RL) based synthetic sample selection method that learns
to choose synthetic images containing reliable and informative features. A
transformer based controller is trained via proximal policy optimization (PPO)
using the validation classification accuracy as the reward. The selected images
are mixed with the original training data for improved training of image
recognition systems. To validate our method, we take the pathology image
recognition as an example and conduct extensive experiments on two
histopathology image datasets. In experiments on a cervical dataset and a lymph
node dataset, the image classification performance is improved by 8.1% and
2.3%, respectively, when utilizing high-quality synthetic images selected by
our RL framework. Our proposed synthetic sample selection method is general and
has great potential to boost the performance of various medical image
recognition systems given limited annotation.Comment: MICCAI202
Between-class Learning for Image Classification
In this paper, we propose a novel learning method for image classification
called Between-Class learning (BC learning). We generate between-class images
by mixing two images belonging to different classes with a random ratio. We
then input the mixed image to the model and train the model to output the
mixing ratio. BC learning has the ability to impose constraints on the shape of
the feature distributions, and thus the generalization ability is improved. BC
learning is originally a method developed for sounds, which can be digitally
mixed. Mixing two image data does not appear to make sense; however, we argue
that because convolutional neural networks have an aspect of treating input
data as waveforms, what works on sounds must also work on images. First, we
propose a simple mixing method using internal divisions, which surprisingly
proves to significantly improve performance. Second, we propose a mixing method
that treats the images as waveforms, which leads to a further improvement in
performance. As a result, we achieved 19.4% and 2.26% top-1 errors on
ImageNet-1K and CIFAR-10, respectively.Comment: 11 pages, 8 figures, published as a conference paper at CVPR 201
Sample Mixed-Based Data Augmentation for Domestic Audio Tagging
Audio tagging has attracted increasing attention since last decade and has
various potential applications in many fields. The objective of audio tagging
is to predict the labels of an audio clip. Recently deep learning methods have
been applied to audio tagging and have achieved state-of-the-art performance,
which provides a poor generalization ability on new data. However due to the
limited size of audio tagging data such as DCASE data, the trained models tend
to result in overfitting of the network. Previous data augmentation methods
such as pitch shifting, time stretching and adding background noise do not show
much improvement in audio tagging. In this paper, we explore the sample mixed
data augmentation for the domestic audio tagging task, including mixup,
SamplePairing and extrapolation. We apply a convolutional recurrent neural
network (CRNN) with attention module with log-scaled mel spectrum as a baseline
system. In our experiments, we achieve an state-of-the-art of equal error rate
(EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming
the baseline system without data augmentation.Comment: submitted to the workshop of Detection and Classification of Acoustic
Scenes and Events 2018 (DCASE 2018), 19-20 November 2018, Surrey, U
Cross-Fertilizing Strategies for Better EM Mountain Climbing and DA Field Exploration: A Graphical Guide Book
In recent years, a variety of extensions and refinements have been developed
for data augmentation based model fitting routines. These developments aim to
extend the application, improve the speed and/or simplify the implementation of
data augmentation methods, such as the deterministic EM algorithm for mode
finding and stochastic Gibbs sampler and other auxiliary-variable based methods
for posterior sampling. In this overview article we graphically illustrate and
compare a number of these extensions, all of which aim to maintain the
simplicity and computation stability of their predecessors. We particularly
emphasize the usefulness of identifying similarities between the deterministic
and stochastic counterparts as we seek more efficient computational strategies.
We also demonstrate the applicability of data augmentation methods for handling
complex models with highly hierarchical structure, using a high-energy
high-resolution spectral imaging model for data from satellite telescopes, such
as the Chandra X-ray Observatory.Comment: Published in at http://dx.doi.org/10.1214/09-STS309 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Paper-based Mixed Reality Sketch Augmentation as a Conceptual Design Support Tool
This undergraduate student paper explores usage of mixed reality techniques as support tools for conceptual design. A proof-of-concept was developed to illustrate this principle. Using this as an example, a small group of designers was interviewed to determine their views on the use of this technology. These interviews are the main contribution of this paper. Several interesting applications were determined, suggesting possible usage in a wide range of domains. Paper-based sketching, mixed reality and sketch augmentation techniques complement each other, and the combination results in a highly intuitive interface
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