7,758 research outputs found
It Takes (Only) Two: Adversarial Generator-Encoder Networks
We present a new autoencoder-type architecture that is trainable in an
unsupervised mode, sustains both generation and inference, and has the quality
of conditional and unconditional samples boosted by adversarial learning.
Unlike previous hybrids of autoencoders and adversarial networks, the
adversarial game in our approach is set up directly between the encoder and the
generator, and no external mappings are trained in the process of learning. The
game objective compares the divergences of each of the real and the generated
data distributions with the prior distribution in the latent space. We show
that direct generator-vs-encoder game leads to a tight coupling of the two
components, resulting in samples and reconstructions of a comparable quality to
some recently-proposed more complex architectures
Linking generative semi-supervised learning and generative open-set recognition
This study investigates the relationship between semi-supervised learning
(SSL) and open-set recognition (OSR) in the context of generative adversarial
networks (GANs). Although no previous study has formally linked SSL and OSR,
their respective methods share striking similarities. Specifically, SSL-GANs
and OSR-GANs require their generators to produce samples in the complementary
space. Subsequently, by regularising networks with generated samples, both SSL
and OSR classifiers generalize the open space. To demonstrate the connection
between SSL and OSR, we theoretically and experimentally compare
state-of-the-art SSL-GAN methods with state-of-the-art OSR-GAN methods. Our
results indicate that the SSL optimised margin-GANs, which have a stronger
foundation in literature, set the new standard for the combined SSL-OSR task
and achieves new state-of-other art results in certain general OSR experiments.
However, the OSR optimised adversarial reciprocal point (ARP)-GANs still
slightly out-performed margin-GANs at other OSR experiments. This result
indicates unique insights for the combined optimisation task of SSL-OSR
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