Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication

• URL: http://arxiv.org/abs/2009.06110v2
• Date: Sat, 17 Jul 2021 12:03:04 GMT
• Title: Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication
• Authors: Ga\v{s}per Begu\v{s}
• Abstract summary: We use the ciwGAN architecture Beguvs in which learning of meaningful representations in speech emerges from a requirement that the CNNs generate informative data. We propose a technique to wug-test CNNs trained on speech and, based on four generative tests, argue that the network learns to represent an identity-based pattern in its latent space.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper models unsupervised learning of an identity-based pattern (or copying) in speech called reduplication from raw continuous data with deep convolutional neural networks. We use the ciwGAN architecture Begu\v{s} (2021a; arXiv:2006.02951) in which learning of meaningful representations in speech emerges from a requirement that the CNNs generate informative data. We propose a technique to wug-test CNNs trained on speech and, based on four generative tests, argue that the network learns to represent an identity-based pattern in its latent space. By manipulating only two categorical variables in the latent space, we can actively turn an unreduplicated form into a reduplicated form with no other substantial changes to the output in the majority of cases. We also argue that the network extends the identity-based pattern to unobserved data. Exploration of how meaningful representations of identity-based patterns emerge in CNNs and how the latent space variables outside of the training range correlate with identity-based patterns in the output has general implications for neural network interpretability.

Related papers

• Coding schemes in neural networks learning classification tasks [52.22978725954347]
  We investigate fully-connected, wide neural networks learning classification tasks. We show that the networks acquire strong, data-dependent features. Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
  arXiv  Detail & Related papers  (2024-06-24T14:50:05Z)
• Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker [42.057348666938736]
  This paper shows a new method to perform supervised learning on Spiking Neural Networks (SNNs), using Spike Timing Dependent Plasticity (STDP) and homeostasis. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks.
  arXiv  Detail & Related papers  (2023-12-05T10:53:31Z)
• Measures of Information Reflect Memorization Patterns [53.71420125627608]
  We show that the diversity in the activation patterns of different neurons is reflective of model generalization and memorization. Importantly, we discover that information organization points to the two forms of memorization, even for neural activations computed on unlabelled in-distribution examples.
  arXiv  Detail & Related papers  (2022-10-17T20:15:24Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Dynamic Inference with Neural Interpreters [72.90231306252007]
  We present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules. inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. We show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner.
  arXiv  Detail & Related papers  (2021-10-12T23:22:45Z)
• Robust Generalization of Quadratic Neural Networks via Function Identification [19.87036824512198]
  Generalization bounds from learning theory often assume that the test distribution is close to the training distribution. We show that for quadratic neural networks, we can identify the function represented by the model even though we cannot identify its parameters.
  arXiv  Detail & Related papers  (2021-09-22T18:02:00Z)
• Category-Learning with Context-Augmented Autoencoder [63.05016513788047]
  Finding an interpretable non-redundant representation of real-world data is one of the key problems in Machine Learning. We propose a novel method of using data augmentations when training autoencoders. We train a Variational Autoencoder in such a way, that it makes transformation outcome predictable by auxiliary network.
  arXiv  Detail & Related papers  (2020-10-10T14:04:44Z)
• Understanding the Role of Individual Units in a Deep Neural Network [85.23117441162772]
  We present an analytic framework to systematically identify hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes.
  arXiv  Detail & Related papers  (2020-09-10T17:59:10Z)
• The Projected Belief Network Classfier : both Generative and Discriminative [13.554038901140949]
  The projected belief network (PBN) is a layered generative network with tractable likelihood function. In this paper, a convolutional PBN is constructed that is both fully discriminative and fully generative.
  arXiv  Detail & Related papers  (2020-08-14T16:00:54Z)
• Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networks [0.0]
  Training deep neural networks on well-understood dependencies in speech data can provide new insights into how they learn internal representations. This paper argues that acquisition of speech can be modeled as a dependency between random space and generated speech data in the Generative Adversarial Network architecture. We propose a methodology to uncover the network's internal representations that correspond to phonetic and phonological properties.
  arXiv  Detail & Related papers  (2020-06-06T20:31:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.