NSML: Meet the MLaaS platform with a real-world case study

Kim, Hanjoo; Kim, Minkyu; Seo, Dongjoo; Kim, Jinwoong; Park, Heungseok; Park, Soeun; Jo, Hyunwoo; Kim, KyungHyun; Yang, Youngil; Sung, Nako; Ha, Jung-Woo

doi:10.48550/arxiv.1810.09957

Cited by 71 publications

(41 citation statements)

References 13 publications

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“…All the experiments are implemented and performed based on NAVER Smart Machine Learning (NSML) platform [13,29] with AutoML [14,25].…”

Section: Methodsmentioning

confidence: 99%

M2FN: Multi-step Modality Fusion for Advertisement Image Assessment

Park,

Ha,

Lee

et al. 2021

Preprint

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Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in ad images. We, therefore, investigated the influence of these attributes on ad image preferences. First, we analyzed large-scale realworld ad log data and, based on our findings, proposed a novel multi-step modality fusion network (M2FN) that determines advertising images likely to appeal to user preferences. Our method utilizes auxiliary attributes through multiple steps in the network, which include conditional batch normalization-based low-level fusion and attention-based high-level fusion. We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world ad dataset with rich auxiliary attributes.

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“…All the experiments are implemented and performed based on NAVER Smart Machine Learning (NSML) platform [13,29] with AutoML [14,25].…”

Section: Methodsmentioning

confidence: 99%

M2FN: Multi-step Modality Fusion for Advertisement Image Assessment

Park,

Ha,

Lee

et al. 2021

Preprint

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“…This work has experimented on the NAVER Smart Machine Learning (NSML) platform (Sung et al, 2017;Kim et al, 2018). We are especially thankful for NAVER AI Lab researchers and Jun-Yan Zhu for the constructive discussions.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Contrastive Fine-grained Class Clustering via Generative Adversarial Networks

Kim¹,

Ha²

2021

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Unsupervised fine-grained class clustering is practical yet challenging task due to the difficulty of feature representations learning of subtle object details. We introduce C3-GAN, a method that leverages the categorical inference power of InfoGAN by applying contrastive learning. We aim to learn feature representations that encourage the data to form distinct cluster boundaries in the embedding space, while also maximizing the mutual information between the latent code and its observation. Our approach is to train the discriminator, which is used for inferring clusters, to optimize the contrastive loss, where the image-latent pairs that maximize the mutual information are considered as positive pairs and the rest as negative pairs. Specifically, we map the input of the generator, which has sampled from the categorical distribution, to the embedding space of the discriminator and let them act as a cluster centroid. In this way, C3-GAN achieved to learn a clustering-friendly embedding space where each cluster is distinctively separable. Experimental results show that C3-GAN achieved state-of-the-art clustering performance on four finegrained benchmark datasets, while also alleviating the mode collapse phenomenon.Code is available at https://github.com/naver-ai/c3-gan.

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“…We report the mean performance of three independent runs. Our experiments were mainly performed based on NAVER Smart Machine Learning platform (NSML) [61], [62]. All the experiments use PyTorch [63] and the geometric deep learning extension library provided by Fey & Lenssen [64].…”

Section: Settingsmentioning

confidence: 99%

Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning

Hwang¹,

Park²,

Kwon³

et al. 2021

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In recent years, graph neural networks (GNNs) have been widely adopted in representation learning of graph-structured data and provided state-of-the-art performance in various application such as link prediction and node classification. Simultaneously, self-supervised learning has been studied to some extent to leverage rich unlabeled data in representation learning on graphs. However, employing self-supervision tasks as auxiliary tasks to assist a primary task has been less explored in the literature on graphs. In this paper, we propose a novel self-supervised auxiliary learning framework to effectively learn graph neural networks. Moreover, we design first a meta-path prediction as a self-supervised auxiliary task for heterogeneous graphs. Our method is learning to learn a primary task with various auxiliary tasks to improve generalization performance. The proposed method identifies an effective combination of auxiliary tasks and automatically balances them to improve the primary task. Our methods can be applied to any graph neural networks in a plug-in manner without manual labeling or additional data. Also, it can be extended to any other auxiliary tasks. Our experiments demonstrate that the proposed method consistently improves the performance of link prediction and node classification on heterogeneous graphs.

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NSML: Meet the MLaaS platform with a real-world case study

Cited by 71 publications

References 13 publications

M2FN: Multi-step Modality Fusion for Advertisement Image Assessment

M2FN: Multi-step Modality Fusion for Advertisement Image Assessment

Contrastive Fine-grained Class Clustering via Generative Adversarial Networks

Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning

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