Disentangling factors of variation with cycle-consistent variational auto-encoders
Generative models that learn disentangled representations for different factors of variation in
an image can be very useful for targeted data augmentation. By sampling from the
disentangled latent subspace of interest, we can efficiently generate new data necessary for
a particular task. Learning disentangled representations is a challenging problem,
especially when certain factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary subspaces by …
an image can be very useful for targeted data augmentation. By sampling from the
disentangled latent subspace of interest, we can efficiently generate new data necessary for
a particular task. Learning disentangled representations is a challenging problem,
especially when certain factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary subspaces by …
Disentangling factors of variation with cycle-consistent variational auto-encoders
Generative models that learn disentangled representations for different factors of variation in
an image can be very useful for targeted data augmentation. By sampling from the
disentangled latent subspace of interest, we can efficiently generate new data necessary for
a particular task. Learning disentangled representations is a challenging problem,
especially when certain factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary subspaces by …
an image can be very useful for targeted data augmentation. By sampling from the
disentangled latent subspace of interest, we can efficiently generate new data necessary for
a particular task. Learning disentangled representations is a challenging problem,
especially when certain factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary subspaces by …
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