Generative models with kernel distance in data space

Knop, Szymon; Mazur, Marcin; Spurek, Przemysław; Tabor, Jacek; Podolak, Igor T.

doi:10.1016/j.neucom.2022.02.053

Cited by 11 publications

(2 citation statements)

References 1 publication

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“…Thus, in the variational circuit, we search for such a kernel that minimizes J with respect to circuit parameters. However, the circuit parameters appear with some weights which must be found by a classical algorithm, as in the case of standard applications of kernel methods.Thus, the SQGEN circuit could be considered as a generative kernel learning method which are being currently studied as a promising tool for generative learning 38 .…”

Section: Discussionmentioning

confidence: 99%

Synergic quantum generative machine learning

Bartkiewicz

Tulewicz

Roik

et al. 2023

Preprint

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We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparametersand report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration betweenthe generators and discriminator, thus, we call it quantum synergic generative learning. We present numerical evidence thatthe synergic approach, in some cases, compares favorably to recently proposed quantum generative adversarial learning.In addition to the results obtained with quantum simulators, we also present experimental results obtained with an actualprogrammable quantum computer. We investigate how a quantum computer implementing generative learning algorithm couldlearn the concept of a maximally- entangled state. After completing the learning process, the network is able both to recognizeand to generate an entangled state. Our approach can be treated as one possible preliminary step to understanding how theconcept of quantum entanglement can be learned and demonstrated by a quantum computer.

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Section: Discussionmentioning

confidence: 99%

Synergic quantum generative machine learning

Bartkiewicz

Tulewicz

Roik

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Synergic quantum generative machine learning

Bartkiewicz

Tulewicz

Roik

et al. 2023

Sci Rep

View full text Add to dashboard Cite

We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparameters and report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration between the generators and discriminator, thus, we call it quantum synergic generative learning. We present numerical evidence that the synergic approach, in some cases, compares favorably to recently proposed quantum generative adversarial learning. In addition to the results obtained with quantum simulators, we also present experimental results obtained with an actual programmable quantum computer. We investigate how a quantum computer implementing generative learning algorithm could learn the concept of a maximally-entangled state. After completing the learning process, the network is able both to recognize and to generate an entangled state. Our approach can be treated as one possible preliminary step to understanding how the concept of quantum entanglement can be learned and demonstrated by a quantum computer.

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Brain Tumor Synthetic Data Generation with Adaptive StyleGANs

Tariq

Qureshi

Zafar

et al. 2023

Communications in Computer and Information Science

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Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data.

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Generative models with kernel distance in data space

Cited by 11 publications

References 1 publication

Synergic quantum generative machine learning

Synergic quantum generative machine learning

Synergic quantum generative machine learning

Brain Tumor Synthetic Data Generation with Adaptive StyleGANs

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