70,537 research outputs found
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning
Deep learning has been achieving decent performance in computer vision
requiring a large volume of images, however, collecting images is expensive and
difficult in many scenarios. To alleviate this issue, many image augmentation
algorithms have been proposed as effective and efficient strategies.
Understanding current algorithms is essential to find suitable methods or
develop novel techniques for given tasks. In this paper, we perform a
comprehensive survey on image augmentation for deep learning with a novel
informative taxonomy. To get the basic idea why we need image augmentation, we
introduce the challenges in computer vision tasks and vicinity distribution.
Then, the algorithms are split into three categories; model-free, model-based,
and optimizing policy-based. The model-free category employs image processing
methods while the model-based method leverages trainable image generation
models. In contrast, the optimizing policy-based approach aims to find the
optimal operations or their combinations. Furthermore, we discuss the current
trend of common applications with two more active topics, leveraging different
ways to understand image augmentation, such as group and kernel theory, and
deploying image augmentation for unsupervised learning. Based on the analysis,
we believe that our survey gives a better understanding helpful to choose
suitable methods or design novel algorithms for practical applications.Comment: Revisio
Correlating neural and symbolic representations of language
Analysis methods which enable us to better understand the representations and
functioning of neural models of language are increasingly needed as deep
learning becomes the dominant approach in NLP. Here we present two methods
based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which
allow us to directly quantify how strongly the information encoded in neural
activation patterns corresponds to information represented by symbolic
structures such as syntax trees. We first validate our methods on the case of a
simple synthetic language for arithmetic expressions with clearly defined
syntax and semantics, and show that they exhibit the expected pattern of
results. We then apply our methods to correlate neural representations of
English sentences with their constituency parse trees.Comment: ACL 201
Spectral Analysis of Kernel and Neural Embeddings: Optimization and Generalization
We extend the recent results of (Arora et al. 2019). by spectral analysis of
the representations corresponding to the kernel and neural embeddings. They
showed that in a simple single-layer network, the alignment of the labels to
the eigenvectors of the corresponding Gram matrix determines both the
convergence of the optimization during training as well as the generalization
properties. We generalize their result to the kernel and neural representations
and show these extensions improve both optimization and generalization of the
basic setup studied in (Arora et al. 2019). In particular, we first extend the
setup with the Gaussian kernel and the approximations by random Fourier
features as well as with the embeddings produced by two-layer networks trained
on different tasks. We then study the use of more sophisticated kernels and
embeddings, those designed optimally for deep neural networks and those
developed for the classification task of interest given the data and the
training labels, independent of any specific classification model
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