How good is my GAN?

Shmelkov, Konstantin; Schmid, Cordelia; Alahari, Karteek

doi:10.48550/arxiv.1807.09499

Cited by 5 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although it has achieved good performance in other fields of research, synthesising data using GAN should be used with attention. According to the results of some studies [103][104][105], there is a mismatch between the data generated by GAN and reality, which can lead to increased misclassification by models trained with synthetic data. To the best of our knowledge, there are no studies that have used GANs to augment grapevine variety identification datasets, however, in the plant context, there are a few that have aimed to identify diseases [106][107][108][109][110].…”

Section: Datasetsmentioning

confidence: 99%

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

Carneiro,

Cunha,

Sousa

2024

Preprint

View full text Add to dashboard Cite

The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

show abstract

Section: Datasetsmentioning

confidence: 99%

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

Carneiro,

Cunha,

Sousa

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Although it has achieved good performance in other fields of research, synthesising data using GAN should be used with attention. According to the results of some studies [79][80][81], there is a mismatch between the data generated by GAN and reality, which can lead to increased misclassification by models trained with synthetic data. To the best of our knowledge, there are no studies that have used GANs to augment grapevine variety identification datasets, however, in the plant context, there are a few that have aimed to identify diseases [82][83][84][85][86].…”

Section: Datasetsmentioning

confidence: 99%

Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review

Carneiro,

Cunha,

Sousa

2024

Preprint

View full text Add to dashboard Cite

The Eurasian grapevine (\textit{Vitis vinifera L.}) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role, as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and control activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) methods have emerged as a classification alternative to deal with the scarcity of ampelographers and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The steps of the standard DL-based classification pipeline were described for the 18 most relevant studies found in the literature, highlighting their pros and cons. Potential directions for improving this field of research were also presented.

show abstract

“…Considering that the ultimate purpose of this work is to improve the classification of real astronomical objects, we naturally adopt the classification accuracy metric first proposed in Yang et al (2017) and later used in Esteban et al (2017), Santurkar et al (2017), Shmelkov et al (2018), and Ravuri & Vinyals (2019). For clarity, we choose to preserve the names in Esteban et al (2017): train on synthetic test on real (TSTR) and train on real test on real (TRTR).…”

Section: Classification Metricsmentioning

confidence: 99%

Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

García-Jara

Protopapas

Estévez

2022

ApJ

View full text Add to dashboard Cite

Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. Currently, classification consists of supervised machine-learning algorithms whose performance is limited by the number of existing annotations of astronomical objects and their highly imbalanced class distributions. In this work, we propose a data augmentation methodology based on generative adversarial networks (GANs) to generate a variety of synthetic light curves from variable stars. Our novel contributions, consisting of a resampling technique and an evaluation metric, can assess the quality of generative models in unbalanced data sets and identify GAN-overfitting cases that the Fréchet inception distance does not reveal. We applied our proposed model to two data sets taken from the Catalina and Zwicky Transient Facility surveys. The classification accuracy of variable stars is improved significantly when training with synthetic data and testing with real data with respect to the case of using only real data.

show abstract

How good is my GAN?

Cited by 5 publications

References 0 publications

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

Deep Learning for Automatic Grapevine Varieties Identification: A Brief Review

Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

Contact Info

Product

Resources

About