Idempotent Generative Network
We propose a new approach for generative modeling based on training a neural
network to be idempotent. An idempotent operator is one that can be applied
sequentially without changing the result beyond the initial application, namely
f(f(z))=f(z). The proposed model f is trained to map a source distribution
(e.g, Gaussian noise) to a target distribution (e.g. realistic images) using
the following objectives: (1) Instances from the target distribution should map
to themselves, namely f(x)=x. We define the target manifold as the set of all
instances that f maps to themselves. (2) Instances that form the source
distribution should map onto the defined target manifold. This is achieved by
optimizing the idempotence term, f(f(z))=f(z) which encourages the range of
f(z) to be on the target manifold. Under ideal assumptions such a process
provably converges to the target distribution. This strategy results in a model
capable of generating an output in one step, maintaining a consistent latent
space, while also allowing sequential applications for refinement.
Additionally, we find that by processing inputs from both target and source
distributions, the model adeptly projects corrupted or modified data back to
the target manifold. This work is a first step towards a ``global projector''
that enables projecting any input into a target data distribution.
6545179d3be12fed568b3d50
Assaf Shocher
claimed_verified
2023-11-04T10:17:32.968Z
6545241378875dadbfeb79b9
/avatars/17bfb7a8959ad8a78f14d79e53b6423e.svg
Assaf Shocher
user
Assafshocher
6545179d3be12fed568b3d51
Amil Dravid
admin_assigned
2023-11-03T16:07:09.482Z
6352e240a0da8800db964820
/avatars/4a70851376faac02a6d4e70da55fc510.svg
Amil Dravid
user
avd4292
6545179d3be12fed568b3d52
Yossi Gandelsman
admin_assigned
2023-11-03T16:07:18.146Z
62f6942205ca68c0e0ff7b97
/avatars/b0600de531b4c88f4aa7c30c5ceb06e1.svg
Yossi Gandelsman
user
yossig
6545179d3be12fed568b3d53
Inbar Mosseri
6545179d3be12fed568b3d54
Michael Rubinstein
6545179d3be12fed568b3d55
Alexei A. Efros
Idempotent Generative Network
We propose a new approach for generative modeling based on training a neural
network to be idempotent. An idempotent operator is one that can be applied
sequentially without changing the result beyond the initial application, namely
f(f(z))=f(z). The proposed model f is trained to map a source distribution
(e.g, Gaussian noise) to a target distribution (e.g. realistic images) using
the following objectives: (1) Instances from the target distribution should map
to themselves, namely f(x)=x. We define the target manifold as the set of all
instances that f maps to themselves. (2) Instances that form the source
distribution should map onto the defined target manifold. This is achieved by
optimizing the idempotence term, f(f(z))=f(z) which encourages the range of
f(z) to be on the target manifold. Under ideal assumptions such a process
provably converges to the target distribution. This strategy results in a model
capable of generating an output in one step, maintaining a consistent latent
space, while also allowing sequential applications for refinement.
Additionally, we find that by processing inputs from both target and source
distributions, the model adeptly projects corrupted or modified data back to
the target manifold. This work is a first step towards a ``global projector''
that enables projecting any input into a target data distribution.
A new generative modeling approach uses an idempotent neural network to project data from a source distribution to a target distribution while maintaining consistency and allowing for refinement.
idempotent neural network generative modeling target manifold source distribution idempotence term