Presents HABP to emphasize hard samples during training and Deact to generate stable synthetic samples for rare attributes, outperforming prior methods on large-scale fashion datasets without extra supervision.
Deep multimodal fusion for persuasiveness prediction
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.
verdicts
UNVERDICTED 2representative citing papers
A multimodal generative model replaces Gaussians with t-distributions and uses gamma-power divergence to improve semi-supervised classification performance on imbalanced partially labeled data.
citing papers explorer
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Hard-Aware Fashion Attribute Classification
Presents HABP to emphasize hard samples during training and Deact to generate stable synthetic samples for rare attributes, outperforming prior methods on large-scale fashion datasets without extra supervision.
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Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance
A multimodal generative model replaces Gaussians with t-distributions and uses gamma-power divergence to improve semi-supervised classification performance on imbalanced partially labeled data.