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Projected GANs Converge Faster
[article]
2021
arXiv
pre-print
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Our Projected GAN improves image quality, sample efficiency, and convergence speed. ...
Importantly, Projected GANs match the previously lowest FIDs up to 40 times faster, cutting the wall-clock time from 5 days to less than 3 hours given the same computational resources. ...
We implement these baselines and our Projected GANs within the codebase provided by the authors of StyleGAN2-ADA [32] . ...
arXiv:2111.01007v1
fatcat:zelvwjh46bcftkywsuota7im4u
Supplementary Information: Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning
[article]
2020
Zenodo
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S2d show that MSEs result in faster convergence than maximum region Figure S2 . ...
Adding a projecting layer increases the initial rate of convergence; however, it also increases final losses. ...
doi:10.5281/zenodo.4304453
fatcat:22kdnxnpxzaxbbpdopszjf2f44
Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network
[article]
2021
arXiv
pre-print
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Then, we use it to generate samples in sampling-based constrained motion planning algorithms to reduce the necessary projection steps, speeding up the computation. ...
We can see that using GAN speeds up both projection and IK computation significantly, around 2-5 times faster than uniform sampling, even when considering only the successful results. ...
GAN samples only require around 2-4 optimization steps to achieve convergence. ...
arXiv:2011.05717v2
fatcat:c5bz35z2qvc5zhbhe6yo5ofhfy
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems
[article]
2019
arXiv
pre-print
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Here, we propose a new method of deploying a GAN-based prior to solve linear inverse problems using projected gradient descent (PGD). ...
Because the learning of the GAN and projector is decoupled from the measurement operator, our GAN-based projector and recovery algorithm are applicable without retraining to all linear inverse problems ...
Conclusion In this work, we propose a GAN based projection network for faster recovery in linear inverse problems. ...
arXiv:1902.09698v2
fatcat:m7oqxhwp2rchhgzv3df62wx22i
Language Modeling with Generative Adversarial Networks
[article]
2018
arXiv
pre-print
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Furthermore, we present the results of some experiments that indicate better training and convergence of Wasserstein GANs (WGANs) when a weaker regularization term is enforcing the Lipschitz constraint ...
GANs were originally designed to output differentiable values, so discrete language generation is challenging for them which causes high levels of instability in training GANs. ...
It trains faster than GAN and produces more coherent results. Even though it still produce illegible phrases occasionally, it learns much faster not to repeat the same mistakes again. ...
arXiv:1804.02617v1
fatcat:uboxaxkxujbplgtgw5u7gnynsy
GAN-Based Projector for Faster Recovery With Convergence Guarantees in Linear Inverse Problems
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
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Here, we propose a new method of deploying a GAN-based prior to solve linear inverse problems using projected gradient descent (PGD). ...
Because the learning of the GAN and projector is decoupled from the measurement operator, our GAN-based projector and recovery algorithm are applicable without retraining to all linear inverse problems ...
Comparison of Run-time for Recovery
Analysis: Error in Projector
Conclusion In this work, we propose a GAN based projection network for faster recovery in linear inverse problems. ...
doi:10.1109/iccv.2019.00570
dblp:conf/iccv/RajLB19
fatcat:bop37m6m4fdrrjeh4anobb553q
Training Generative Adversarial Networks with Adaptive Composite Gradient
[article]
2021
arXiv
pre-print
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Theory and toy-function experiments suggest that our approach can alleviate the cyclic behaviors and converge faster than recently proposed algorithms. ...
We conducted two mixture of Gaussians experiments by integrating ACG to existing algorithms with Linear GANs. Results show ACG is competitive with the previous algorithms. ...
However, the other methods all converge to the Nash Equilibrium. We proposed the ACG method converges faster than other convergence methods. ...
arXiv:2111.05508v1
fatcat:x4ymv2zt6nc6xdxmgyne6e75p4
Megapixel Size Image Creation using Generative Adversarial Networks
[article]
2017
arXiv
pre-print
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In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry ...
Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. ...
Acknowledgement This research was part of a commercial project funded by MHPC. ...
arXiv:1706.00082v1
fatcat:wahjgbltbzdtfl77pvyg35zm24
Fast learning from label proportions with small bags
[article]
2021
arXiv
pre-print
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In comparison to existing deep LLP methods, our approach converges faster to a comparable or better solution. Several experiments were performed on two different datasets. ...
Acknowledgements The authors acknowledge the support of the OP VVV funded project "CZ.02.1.01/0.0/0.0/16_019/0000765 Research Center for Informatics" and the Grant Agency of the Czech Technical University ...
Faster convergence is especially desirable when big datasets consisting of highdimensional instances are processed. ...
arXiv:2110.03426v2
fatcat:3pym4sw5kvacvdun2rfa76psze
Inverse mapping of face GANs
[article]
2020
arXiv
pre-print
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Generative adversarial networks (GANs) synthesize realistic images from a random latent vector. ...
In addition, our proposed method projects generated faces to their latent-space with high fidelity and speed. At last, we demonstrate the performance of our approach on both real and generated faces. ...
Methods focusing on projecting images into the corresponding GAN latent vectors can be categorized into four major groups. ...
arXiv:2009.05671v1
fatcat:h744nq724zblflol6pk4jeatme
A study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance
[article]
2018
arXiv
pre-print
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This approach could produce a promising output along with a faster and more stable training for the model. ...
The BEGAN model additionally has a balanced generator-discriminator combination, which prevents mode collapse along with a global convergence measure. ...
A recent work on Face Frontalization using GAN (FFGAN) takes the help of 3D morphable model coefficients to aid with faster training and better convergence [15] .
V. ...
arXiv:1809.05611v1
fatcat:2xyqoet7ljhhnlueeq265sjxri
Training GANs with predictive projection centripetal acceleration
[article]
2020
arXiv
pre-print
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Although remarkable successful in practice, training generative adversarial networks(GANs) is still quite difficult and iteratively prone to cyclic behaviors, as GANs need to solve a non-convex non-concave ...
Motivated by the ideas of simultaneous centripetal acceleration (SCA) and modified predictive methods (MPM), we propose a novel predictive projection centripetal acceleration (PPCA) methods to alleviate ...
Both PPCA and APPCA (γ = 1, α = 0.1, β = 0.3) also exponentially converge to the origin and the later seems faster. ...
arXiv:2010.03322v2
fatcat:7xqoiznjfbg2tfp26oqvyknjmy
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
[article]
2018
arXiv
pre-print
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Second, the resulting modified interpolator is used for extrapolation where the constraints are enforced after each step through projection on the space of constraints. ...
Generative Adversarial Networks (GANs) are becoming popular choices for unsupervised learning. ...
As we have mentioned before, the practice of training GANs has shown that the discriminator can train faster and more effectively than the generator. ...
arXiv:1803.08182v1
fatcat:6qjxn63nejf75oigtqwkam7zki
Correlation between surface morphologies and crystallographic structures of GaN layers grown by MOCVD on sapphire
1996
MRS Internet Journal of Nitride Semiconductor Research
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Convergent Beam Electron Diffraction studies were particularly important to determine the polarity of the GaN layers. ...
These pyramids are formed when the tiny Ga-polar IDs grow faster than the surrounding N-polar matrix. Flat GaN layers are unipolar, with a Ga polarity. ...
Kaufmann, the coordinator of this European project. We thank Dr A. Bourret and Dr B. Daudin for helpful discussions. ...
doi:10.1557/s1092578300002052
fatcat:qi54zueexvdxhkand4q2aomcle
AutoLoss: Learning Discrete Schedules for Alternate Optimization
[article]
2018
arXiv
pre-print
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Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. ...
We apply AutoLoss on four ML tasks: d-ary quadratic regression, classification using a multi-layer perceptron (MLP), image generation using GANs, and multi-task neural machine translation (NMT). ...
For example, comparing to GAN 1:1, AUTOLOSS improves the converged IS for 0.5, and is almost 3x faster to achieve where GAN 1:1 converges (IS = 8.6) in average. ...
arXiv:1810.02442v1
fatcat:hxxbjrl5s5ad5jekolgnrthdle
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