1,036 research outputs found
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods
Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews
AI Methods in Algorithmic Composition: A Comprehensive Survey
Algorithmic composition is the partial or total automation of the process of music composition
by using computers. Since the 1950s, different computational techniques related to
Artificial Intelligence have been used for algorithmic composition, including grammatical
representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint
programming and evolutionary algorithms. This survey aims to be a comprehensive
account of research on algorithmic composition, presenting a thorough view of the field for
researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project
(IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e InnovaciΓ³n, and a grant for
the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo
y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC-
5123) from the ConsejerΓa de InnovaciΓ³n y Ciencia de AndalucΓa
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μ μ°½μμ±μ μμ μ μΈ μμ± νμ§λ‘ νμ₯ν μ μλ€λ κ°λ₯μ±μ μμ¬νλ€.Chapter 1 Introduction 1
1.1 Motivation 5
1.2 Definitions 8
1.3 Tasks of Interest 10
1.3.1 Generation Quality 10
1.3.2 Controllability 12
1.4 Approaches 13
1.4.1 Modeling Musical Hierarchy 14
1.4.2 Regularizing Latent Representations 16
1.4.3 Target Tasks 18
1.5 Outline of the Thesis 19
Chapter 2 Background 22
2.1 Music Generation Tasks 23
2.1.1 Melody Harmonization 23
2.1.2 Expressive Performance Rendering 25
2.2 Structure-enhanced Music Generation 27
2.2.1 Hierarchical Music Generation 27
2.2.2 Transformer-based Music Generation 28
2.3 Disentanglement Learning 29
2.3.1 Unsupervised Approaches 30
2.3.2 Supervised Approaches 30
2.3.3 Self-supervised Approaches 31
2.4 Controllable Music Generation 32
2.4.1 Score Generation 32
2.4.2 Performance Rendering 33
2.5 Summary 34
Chapter 3 Translating Melody to Chord: Structured and Flexible Harmonization of Melody with Transformer 36
3.1 Introduction 36
3.2 Proposed Methods 41
3.2.1 Standard Transformer Model (STHarm) 41
3.2.2 Variational Transformer Model (VTHarm) 44
3.2.3 Regularized Variational Transformer Model (rVTHarm) 46
3.2.4 Training Objectives 47
3.3 Experimental Settings 48
3.3.1 Datasets 49
3.3.2 Comparative Methods 50
3.3.3 Training 50
3.3.4 Metrics 51
3.4 Evaluation 56
3.4.1 Chord Coherence and Diversity 57
3.4.2 Harmonic Similarity to Human 59
3.4.3 Controlling Chord Complexity 60
3.4.4 Subjective Evaluation 62
3.4.5 Qualitative Results 67
3.4.6 Ablation Study 73
3.5 Conclusion and Future Work 74
Chapter 4 Sketching the Expression: Flexible Rendering of Expressive Piano Performance with Self-supervised Learning 76
4.1 Introduction 76
4.2 Proposed Methods 79
4.2.1 Data Representation 79
4.2.2 Modeling Musical Hierarchy 80
4.2.3 Overall Network Architecture 81
4.2.4 Regularizing the Latent Variables 84
4.2.5 Overall Objective 86
4.3 Experimental Settings 87
4.3.1 Dataset and Implementation 87
4.3.2 Comparative Methods 88
4.4 Evaluation 88
4.4.1 Generation Quality 89
4.4.2 Disentangling Latent Representations 90
4.4.3 Controllability of Expressive Attributes 91
4.4.4 KL Divergence 93
4.4.5 Ablation Study 94
4.4.6 Subjective Evaluation 95
4.4.7 Qualitative Examples 97
4.4.8 Extent of Control 100
4.5 Conclusion 102
Chapter 5 Conclusion and Future Work 103
5.1 Conclusion 103
5.2 Future Work 106
5.2.1 Deeper Investigation of Controllable Factors 106
5.2.2 More Analysis of Qualitative Evaluation Results 107
5.2.3 Improving Diversity and Scale of Dataset 108
Bibliography 109
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European Union regulations on algorithmic decision-making and a "right to explanation"
We summarize the potential impact that the European Union's new General Data
Protection Regulation will have on the routine use of machine learning
algorithms. Slated to take effect as law across the EU in 2018, it will
restrict automated individual decision-making (that is, algorithms that make
decisions based on user-level predictors) which "significantly affect" users.
The law will also effectively create a "right to explanation," whereby a user
can ask for an explanation of an algorithmic decision that was made about them.
We argue that while this law will pose large challenges for industry, it
highlights opportunities for computer scientists to take the lead in designing
algorithms and evaluation frameworks which avoid discrimination and enable
explanation.Comment: presented at 2016 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2016), New York, N
Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task
Benefiting from large-scale datasets and pre-trained models, the field of
generative models has recently gained significant momentum. However, most
datasets for symbolic music are very small, which potentially limits the
performance of data-driven multimodal models. An intuitive solution to this
problem is to leverage pre-trained models from other modalities (e.g., natural
language) to improve the performance of symbolic music-related multimodal
tasks. In this paper, we carry out the first study of generating complete and
semantically consistent symbolic music scores from text descriptions, and
explore the efficacy of using publicly available checkpoints (i.e., BERT,
GPT-2, and BART) for natural language processing in the task of text-to-music
generation. Our experimental results show that the improvement from using
pre-trained checkpoints is statistically significant in terms of BLEU score and
edit distance similarity. We analyse the capabilities and limitations of our
model to better understand the potential of language-music models.Comment: 5 pages, 2 figures, 2 table
One-shot Detail Retouching with Patch Space Neural Field based Transformation Blending
Photo retouching is a difficult task for novice users as it requires expert
knowledge and advanced tools. Photographers often spend a great deal of time
generating high-quality retouched photos with intricate details. In this paper,
we introduce a one-shot learning based technique to automatically retouch
details of an input image based on just a single pair of before and after
example images. Our approach provides accurate and generalizable detail edit
transfer to new images. We achieve these by proposing a new representation for
image to image maps. Specifically, we propose neural field based transformation
blending in the patch space for defining patch to patch transformations for
each frequency band. This parametrization of the map with anchor
transformations and associated weights, and spatio-spectral localized patches,
allows us to capture details well while staying generalizable. We evaluate our
technique both on known ground truth filtes and artist retouching edits. Our
method accurately transfers complex detail retouching edits
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