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Z. Zhao
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Controlling Poisson Flow Generative Model
Implementing a class conditional generative model
With the following paper we are planning to present and explore the possibilities of the the newly introduced Poisson Flow Generative Model (PFGM). More specifically, this work aims to introduce the Conditional Poisson Flow Generative Model (CoPFGM), which by extending the existing repository of the PFGM, it will be able to be trained in a way that allows for conditional image sampling. The work aims to provide a more modular solution that can be easily adjusted for multiple data sets, including custom, as well as datasets taken directly from large Python libraries such as PyTorch and TensorFlow. Our proposed CoPFGM consists of two steps: (i) modifying the input of underlying UNet and (ii) modifying the loss function. More specifically, for (i) we have augmented the input channels of every image with one-hot-like class conditional images, and about (ii) we are introducing an updated loss function which incorporates the Cross-Entropy Loss of the generated images during training. The proposed model is tested against 2 datasets, the MNIST, and the Dilbert dataset, the latter, consists of 1100 custom images of the faces of 6 characters taken from the Dilbert Comic-Strip. The proposed model will be tested and presented in the form of an Ablation Study, with which, we show the conditional behavior of the channel augmentation, and the image improvement in terms of class representation with the Cross-Entropy loss.
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With the following paper we are planning to present and explore the possibilities of the the newly introduced Poisson Flow Generative Model (PFGM). More specifically, this work aims to introduce the Conditional Poisson Flow Generative Model (CoPFGM), which by extending the existing repository of the PFGM, it will be able to be trained in a way that allows for conditional image sampling. The work aims to provide a more modular solution that can be easily adjusted for multiple data sets, including custom, as well as datasets taken directly from large Python libraries such as PyTorch and TensorFlow. Our proposed CoPFGM consists of two steps: (i) modifying the input of underlying UNet and (ii) modifying the loss function. More specifically, for (i) we have augmented the input channels of every image with one-hot-like class conditional images, and about (ii) we are introducing an updated loss function which incorporates the Cross-Entropy Loss of the generated images during training. The proposed model is tested against 2 datasets, the MNIST, and the Dilbert dataset, the latter, consists of 1100 custom images of the faces of 6 characters taken from the Dilbert Comic-Strip. The proposed model will be tested and presented in the form of an Ablation Study, with which, we show the conditional behavior of the channel augmentation, and the image improvement in terms of class representation with the Cross-Entropy loss.
Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm that allows multiple parties to train a model collaboratively on vertically partitioned data while maintaining data privacy. The existing design of training autoencoders in VFL is to train a separate autoencoder in each participant and aggregate the latent representation later. This design could potentially break important correlations between feature data of participating parties, as each autoencoder is trained on locally available features while disregarding the features of others. In addition, traditional autoencoders are not specifically designed for tabular data, which is ubiquitous in VFL settings. Moreover, the impact of client failures during training on the model robustness is under-researched in the VFL scene. In this paper, we propose TabVFL, a distributed framework designed to improve latent representation learning using the joint features of participants. The framework (i) preserves privacy by mitigating potential data leakage with the addition of a fully-connected layer, (ii) conserves feature correlations by learning one latent representation vector, and (iii) provides enhanced robustness against client failures during training phase. Extensive experiments on five classification datasets show that TabVFL can outperform the prior work design, with 26.12% of improvement on f1-score.
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Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm that allows multiple parties to train a model collaboratively on vertically partitioned data while maintaining data privacy. The existing design of training autoencoders in VFL is to train a separate autoencoder in each participant and aggregate the latent representation later. This design could potentially break important correlations between feature data of participating parties, as each autoencoder is trained on locally available features while disregarding the features of others. In addition, traditional autoencoders are not specifically designed for tabular data, which is ubiquitous in VFL settings. Moreover, the impact of client failures during training on the model robustness is under-researched in the VFL scene. In this paper, we propose TabVFL, a distributed framework designed to improve latent representation learning using the joint features of participants. The framework (i) preserves privacy by mitigating potential data leakage with the addition of a fully-connected layer, (ii) conserves feature correlations by learning one latent representation vector, and (iii) provides enhanced robustness against client failures during training phase. Extensive experiments on five classification datasets show that TabVFL can outperform the prior work design, with 26.12% of improvement on f1-score.