85 research outputs found
Conditional GANs with Auxiliary Discriminative Classifier
Conditional generative models aim to learn the underlying joint distribution
of data and labels to achieve conditional data generation. Among them, the
auxiliary classifier generative adversarial network (AC-GAN) has been widely
used, but suffers from the problem of low intra-class diversity of the
generated samples. The fundamental reason pointed out in this paper is that the
classifier of AC-GAN is generator-agnostic, which therefore cannot provide
informative guidance for the generator to approach the joint distribution,
resulting in a minimization of the conditional entropy that decreases the
intra-class diversity. Motivated by this understanding, we propose a novel
conditional GAN with an auxiliary discriminative classifier (ADC-GAN) to
resolve the above problem. Specifically, the proposed auxiliary discriminative
classifier becomes generator-aware by recognizing the class-labels of the real
data and the generated data discriminatively. Our theoretical analysis reveals
that the generator can faithfully learn the joint distribution even without the
original discriminator, making the proposed ADC-GAN robust to the value of the
coefficient hyperparameter and the selection of the GAN loss, and stable during
training. Extensive experimental results on synthetic and real-world datasets
demonstrate the superiority of ADC-GAN in conditional generative modeling
compared to state-of-the-art classifier-based and projection-based conditional
GANs.Comment: ICML 202
Learning from small and imbalanced dataset of images using generative adversarial neural networks.
The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and semi-supervised learning have not lived up to expectations. Some of these techniques do not account for other classification challenges, such as a class-imbalance problem. Thus, these techniques mostly underperform when compared with fully supervised approaches. In this thesis, we propose new methods to train a deep model on image classification with a limited number of labeled examples. This was achieved by extending state-of-the-art generative adversarial networks with multiple fake classes and network switchers. These new features enabled us to train a classifier using large unlabeled data, while generating class specific samples. The proposed model is label agnostic and is suitable for different classification scenarios, ranging from weakly supervised to fully supervised settings. This was used to address classification challenges with limited labeled data and a class-imbalance problem. Extensive experiments were carried out on different benchmark datasets. Firstly, the proposed approach was used to train a classification model and our findings indicated that the proposed approach achieved better classification accuracies, especially when the number of labeled samples is small. Secondly, the proposed approach was able to generate high-quality samples from class-imbalance datasets. The samples' quality is evident in improved classification performances when generated samples were used in neutralising class-imbalance. The results are thoroughly analyzed and, overall, our method showed superior performances over popular resampling technique and the AC-GAN model. Finally, we successfully applied the proposed approach as a new augmentation technique to two challenging real-world problems: face with attributes and legacy engineering drawings. The results obtained demonstrate that the proposed approach is effective even in extreme cases
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