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Learning to Traverse Latent Spaces for Musical Score Inpainting

Ashis Pati, Alexander Lerch, Gaëtan Hadjeres
2019 Zenodo  
To this end, a novel deep learning-based approach for musical score inpainting is proposed.  ...  To achieve this, we leverage the representational power of the latent space of a Variational Auto-Encoder and train a Recurrent Neural Network which learns to traverse this latent space conditioned on  ...  "Learning To Traverse Latent Spaces for Musical Score Inpainting", 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, 2019.  ... 
doi:10.5281/zenodo.3527813 fatcat:k3roq22z5bet7l35ibb4rrhquy

Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders [article]

Ashis Pati, Alexander Lerch
2020 arXiv   pre-print
In this paper, we present a novel method to structure the latent space of a Variational Auto-Encoder (VAE) to encode different continuous-valued attributes explicitly.  ...  The results obtained from several quantitative and qualitative experiments show that the proposed method leads to disentangled and interpretable latent spaces that can be used to effectively manipulate  ...  Acknowledgements The authors would like to thank Nvidia Corporation for their donation of a Titan V awarded as part of the GPU (Graphics Processing Unit) grant program which was used for running several  ... 
arXiv:2004.05485v3 fatcat:am3cuewchfes7oqve7gvb2ux6i

On the Development and Practice of AI Technology for Contemporary Popular Music Production

Emmanuel Deruty, Maarten Grachten, Stefan Lattner, Javier Nistal, Cyran Aouameur
2022 Transactions of the International Society for Music Information Retrieval  
Although the use of AI technology for music production is still in its infancy, it has the potential to make a lasting impact on the way we produce music.  ...  Based on this we formulate some recommendations and validation criteria for the development of AI technology for contemporary Popular Music.  ...  The output adapts to the tempo and rhythm of the existing tracks, and users can explore different rhythmic variations by traversing a latent space.  ... 
doi:10.5334/tismir.100 dblp:journals/tismir/DerutyGLNA22 fatcat:f6g36uq6gvfkjdt7sqdk6sdyp4

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Sam Bond-Taylor, Adam Leach, Yang Long, Chris George Willcocks
2021 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples.  ...  In particular, this compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, in addition to numerous hybrid approaches  ...  data space, redundancy is reduced and latents are encouraged to learn global structure [146] .  ... 
doi:10.1109/tpami.2021.3116668 pmid:34591756 fatcat:yjpayhmrfnaeziahmrgiyvtxkm

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models [article]

Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks
2021 arXiv   pre-print
Deep generative modelling is a class of techniques that train deep neural networks to model the distribution of training samples.  ...  These techniques are drawn under a single cohesive framework, comparing and contrasting to explain the premises behind each, while reviewing current state-of-the-art advances and implementations.  ...  data space, redundancy is reduced and latents are encouraged to learn global structure [146] .  ... 
arXiv:2103.04922v2 fatcat:nivlg3whyjhadhwdl2tsh5yciy

Procedural Content Generation via Machine Learning (PCGML) [article]

Adam Summerville, Sam Snodgrass, Matthew Guzdial, Christoffer Holmgård, Amy K. Hoover, Aaron Isaksen, Andy Nealen, Julian Togelius
2018 arXiv   pre-print
In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling  ...  As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm  ...  Another interesting combination of search and machine learning for PCG is the DeLeNoX algorithm, which uses unsupervised learning to continuously reshape the search space and novelty search to search for  ... 
arXiv:1702.00539v3 fatcat:p3whkcq2cbaqzo6pb6tldshu3a

Action2video: Generating Videos of Human 3D Actions [article]

Chuan Guo, Xinxin Zuo, Sen Wang, Xinshuang Liu, Shihao Zou, Minglun Gong, Li Cheng
2021 arXiv   pre-print
It also necessitates the curation and reannotation of 3D human motion datasets for training purpose.  ...  This is realized by improving existing methods to extract 3D human shapes and textures from single 2D images, rigging, animating, and rendering to form 2D videos of human motions.  ...  learned to produce input latent vector for pose gener- motion sequence is segmented into short motion snip- ator to synthesize pose at each time.  ... 
arXiv:2111.06925v2 fatcat:obdpetdfqbdetonei73ndq6ckq

Video Generative Adversarial Networks: A Review

Nuha Aldausari, Arcot Sowmya, Nadine Marcus, Gelareh Mohammadi
2023 ACM Computing Surveys  
While the variations of GANs models in general have been covered to some extent in several survey papers, to the best of our knowledge, this is the first paper that reviews the state-of-the-art video GANs  ...  The conditional models are then further classified according to the provided condition into audio, text, video, and image.  ...  While VGAN [6] and FTGAN [56] map a video to a point in the latent vector, the MoCoGAN framework traverses N latent points, one per frame, where each vector can be decomposed into the motion vector  ... 
doi:10.1145/3487891 fatcat:fssfwvlfsje4ddk5pk5cahdwuu

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4413249 fatcat:35qbhenysfhvza2roihx52afuy

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4429792 fatcat:qs6yuapx4vdbdmwna7ix7nnwty

Review: Deep Learning in Electron Microscopy [article]

Jeffrey M. Ede
2020 arXiv   pre-print
For context, we review popular applications of deep learning in electron microscopy.  ...  Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes.  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
arXiv:2009.08328v4 fatcat:umocfp5dgvfqzck4ontlflh5ca

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4598227 fatcat:hm2ksetmsvf37adjjefmmbakvq

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4591029 fatcat:zn2hvfyupvdwlnvsscdgswayci

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4399748 fatcat:63ggmnviczg6vlnqugbnrexsgy

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional  ...  and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4415407 fatcat:6dejwzzpmfegnfuktrld6zgpiq
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