528 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
Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods are discriminative
approaches, which model the conditional distribution of labels given inputs
features. The success of such approaches heavily depends on high-quality
labeled instances, which are not easy to obtain, especially as the number of
candidate classes increases. In this paper, we study the complementary learning
problem. Unlike ordinary labels, complementary labels are easy to obtain
because an annotator only needs to provide a yes/no answer to a randomly chosen
candidate class for each instance. We propose a generative-discriminative
complementary learning method that estimates the ordinary labels by modeling
both the conditional (discriminative) and instance (generative) distributions.
Our method, we call Complementary Conditional GAN (CCGAN), improves the
accuracy of predicting ordinary labels and can generate high-quality instances
in spite of weak supervision. In addition to the extensive empirical studies,
we also theoretically show that our model can retrieve the true conditional
distribution from the complementarily-labeled data
Towards Generalizable Morph Attack Detection with Consistency Regularization
Though recent studies have made significant progress in morph attack
detection by virtue of deep neural networks, they often fail to generalize well
to unseen morph attacks. With numerous morph attacks emerging frequently,
generalizable morph attack detection has gained significant attention. This
paper focuses on enhancing the generalization capability of morph attack
detection from the perspective of consistency regularization. Consistency
regularization operates under the premise that generalizable morph attack
detection should output consistent predictions irrespective of the possible
variations that may occur in the input space. In this work, to reach this
objective, two simple yet effective morph-wise augmentations are proposed to
explore a wide space of realistic morph transformations in our consistency
regularization. Then, the model is regularized to learn consistently at the
logit as well as embedding levels across a wide range of morph-wise augmented
images. The proposed consistency regularization aligns the abstraction in the
hidden layers of our model across the morph attack images which are generated
from diverse domains in the wild. Experimental results demonstrate the superior
generalization and robustness performance of our proposed method compared to
the state-of-the-art studies.Comment: Accepted to the IEEE International Joint Conference on Biometrics
(IJCB), 202
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