We have recently seen great progress in image classification due to the success of deep convolutional neural networks and the availability of large-scale datasets. Most of the existing work focuses on single-label image classification. However, there are usually multiple tags associated with an image. The existing works on multi-label classification are mainly based on lab curated labels. Humans assign tags to their images differently, which is mainly based on their interests and personal tagging behavior. In this paper, we address the problem of personalized tag recommendation and propose an end-to-end deep network which can be trained on large-scale datasets. The user-preference is learned within the network in an unsupervised way where the network performs joint optimization for user-preference and visual encoding. A joint training of user-preference and visual encoding allows the network to efficiently integrate the visual preference with tagging behavior for a better user recommendation. In addition, we propose the use of adversarial learning, which enforces the network to predict tags resembling user-generated tags. We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100M and NUS-WIDE. The proposed method achieves significantly better performance on both the datasets when compared to the baselines and other state-of-the-art methods. The code is publicly available at https://github.com/vyzuer/ALTReco.

中文翻译：

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个性化标签推荐的对抗学习

由于深度卷积神经网络的成功和大规模数据集的可用性，我们最近在图像分类方面取得了巨大进步。现有的大部分工作都集中在单标签图像分类上。但是，通常有多个标签与图像相关联。现有的多标签分类工作主要基于实验室策划的标签。人类为他们的图像分配不同的标签，这主要基于他们的兴趣和个人标签行为。在本文中，我们解决了个性化标签推荐的问题，并提出了一种可以在大规模数据集上训练的端到端深度网络。用户偏好以无监督的方式在网络内学习，网络对用户偏好和视觉编码进行联合优化。用户偏好和视觉编码的联合训练使网络能够有效地将视觉偏好与标记行为相结合，以获得更好的用户推荐。此外，我们建议使用对抗性学习，它强制网络预测类似于用户生成的标签的标签。我们在两个不同的大规模公开可用数据集 YFCC100M 和 NUS-WIDE 上证明了所提出模型的有效性。与基线和其他最先进的方法相比，所提出的方法在两个数据集上都取得了显着更好的性能。该代码可在 https://github.com/vyzuer/ALTReco 上公开获得。我们建议使用对抗性学习，它强制网络预测类似于用户生成标签的标签。我们在两个不同的大规模公开可用数据集 YFCC100M 和 NUS-WIDE 上证明了所提出模型的有效性。与基线和其他最先进的方法相比，所提出的方法在两个数据集上都取得了显着更好的性能。该代码可在 https://github.com/vyzuer/ALTReco 上公开获得。我们建议使用对抗性学习，它强制网络预测类似于用户生成标签的标签。我们在两个不同的大规模公开可用数据集 YFCC100M 和 NUS-WIDE 上证明了所提出模型的有效性。与基线和其他最先进的方法相比，所提出的方法在两个数据集上都取得了显着更好的性能。该代码可在 https://github.com/vyzuer/ALTReco 上公开获得。