On augmenting database schemas by latent visual attributes

Grošup, Tomáš; Peška, Ladislav; Skopal, Tomáš

doi:10.1007/s10115-021-01595-z

“…FastkNN [152] FCNN [56] FNN [32] Fuzzylogic [106,113] GAN [71,75,99,129,134,141,155,251] GA-RF [65] GaussianMixtureModels [109,118] gradientboostingdecisiontree [42] GraphCNN [40,122] GraphDCNN [89] GRU [230] GSN [87] H-CNN [39,157] HOG [153] HypergraphNN [80] Imageprocessing [119,120,130,132,135,136,147] k-center [117] k-means [114,203,232,244] k-medoids [115] Kneser-Ney [34] kNN [54,137,222,235,237,250] LAC [107,116] Lassoregression …”

Section: Methods Citationsmentioning

confidence: 99%

“…The majority of research focuses on identifying identical or nearly identical clothing or even detecting plagiarism. The use of DCNN algorithms to resolve retrieval tasks has become popular due to deep learning advancements [54,86]. For this objective, generative adversarial networks (GAN) are also helpful [71,75].…”

Section: Garment Representationmentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

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Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

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“…Recent years have witnessed a growing research and application of data augmentation in various domains, such as image analysis [1,2], text mining [3,4] and so on. While machine learning techniques excel in various tasks, they often struggle with variations in data distribution [5].…”

Section: Introductionmentioning

confidence: 99%

PatchMix: patch-level mixup for data augmentation in convolutional neural networks

Hong,

Chen

2024

Knowl Inf Syst

0

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Convolutional neural networks (CNNs) have demonstrated impressive performance in fitting data distribution. However, due to the complexity in learning intricate features from data, networks usually experience overfitting during the training. To address this issue, many data augmentation techniques have been proposed to expand the representation of the training data, thereby improving the generalization ability of CNNs. In this paper, we propose PatchMix, a novel mixup-based augmentation method that applies mixup to patches within an image rather than blending between multiple images. Despite the challenge of label mixing, PatchMix generates numerous reliable training samples. Additionally, we propose PatchMix-R to enhance the robustness of the model against perturbations by processing adjacent pixels. Easy to implement, our methods can be integrated with most CNN-based classification models and combined with varying data augmentation techniques. The experiments show that PatchMix and PatchMix-R consistently outperform other state-of-the-art methods in terms of accuracy and robustness. Class activation mappings (CAMs) of the trained model are also investigated to visualize the effectiveness of our approach.

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PatchMix: Patch-Level Mixup for Data Augmentation in Convolutional Neural Networks

Hong,

Chen

2023

Preprint

0

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Convolutional neural networks (CNNs) have demonstrated impressive performance in fitting data distribution. However, due to the complexity in learning intricate features from data, networks usually experience overfitting during the training. To address this issue, many data augmentation techniques have been proposed to expand the representation of the training data, thereby improving the generalization ability of CNNs. In this paper, we propose PatchMix, a novel mixup-based augmentation method that applies mixup to patches within an image rather than blending between multiple images. Despite the challenge of label mixing, PatchMix generates numerous reliable training samples. Additionally, we propose PatchMix-R to enhance the robustness of the model against perturbations by processing adjacent pixels. Easy to implement, our methods can be integrated with most CNN-based classification models and combined with varying data augmentation techniques. The experiments show that PatchMix and PatchMix-R consistently outperform other state-of-the-art methods in terms of accuracy and robustness. Class activation mappings (CAMs) of the trained model are also investigated to visualize the effectiveness of our approach.

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On augmenting database schemas by latent visual attributes

Cited by 3 publications

References 51 publications

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

PatchMix: patch-level mixup for data augmentation in convolutional neural networks

PatchMix: Patch-Level Mixup for Data Augmentation in Convolutional Neural Networks

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