CoopInit: Initializing Generative Adversarial Networks via Cooperative
Learning
- URL: http://arxiv.org/abs/2303.11649v1
- Date: Tue, 21 Mar 2023 07:49:32 GMT
- Title: CoopInit: Initializing Generative Adversarial Networks via Cooperative
Learning
- Authors: Yang Zhao, Jianwen Xie, Ping Li
- Abstract summary: CoopInit is a cooperative learning-based strategy that can quickly learn a good starting point for GANs.
We demonstrate the effectiveness of the proposed approach on image generation and one-sided unpaired image-to-image translation tasks.
- Score: 50.90384817689249
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Numerous research efforts have been made to stabilize the training of the
Generative Adversarial Networks (GANs), such as through regularization and
architecture design. However, we identify the instability can also arise from
the fragile balance at the early stage of adversarial learning. This paper
proposes the CoopInit, a simple yet effective cooperative learning-based
initialization strategy that can quickly learn a good starting point for GANs,
with a very small computation overhead during training. The proposed algorithm
consists of two learning stages: (i) Cooperative initialization stage: The
discriminator of GAN is treated as an energy-based model (EBM) and is optimized
via maximum likelihood estimation (MLE), with the help of the GAN's generator
to provide synthetic data to approximate the learning gradients. The EBM also
guides the MLE learning of the generator via MCMC teaching; (ii) Adversarial
finalization stage: After a few iterations of initialization, the algorithm
seamlessly transits to the regular mini-max adversarial training until
convergence. The motivation is that the MLE-based initialization stage drives
the model towards mode coverage, which is helpful in alleviating the issue of
mode dropping during the adversarial learning stage. We demonstrate the
effectiveness of the proposed approach on image generation and one-sided
unpaired image-to-image translation tasks through extensive experiments.
Related papers
- Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation [46.5310645609264]
We propose a Meta-learning and Markov Chain Monte Carlo based SISR approach to learn kernel priors from organized randomness.
A lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions.
A meta-learning-based alternating optimization procedure is proposed to optimize the kernel generator and image restorer.
arXiv Detail & Related papers (2024-06-13T07:50:15Z) - Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification [3.0398616939692777]
Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have become standard.
The study aims to elucidate the advantages of pre-training techniques and fine-tuning strategies to enhance the learning process of neural networks.
arXiv Detail & Related papers (2024-05-29T15:44:51Z) - Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood [64.95663299945171]
Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming.
There exists a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models.
We propose cooperative diffusion recovery likelihood (CDRL), an effective approach to tractably learn and sample from a series of EBMs.
arXiv Detail & Related papers (2023-09-10T22:05:24Z) - Guiding The Last Layer in Federated Learning with Pre-Trained Models [18.382057374270143]
Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a number of participants without sharing data.
We show that fitting a classification head using the Nearest Class Means (NCM) can be done exactly and orders of magnitude more efficiently than existing proposals.
arXiv Detail & Related papers (2023-06-06T18:02:02Z) - Stochastic Unrolled Federated Learning [85.6993263983062]
We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning.
Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
arXiv Detail & Related papers (2023-05-24T17:26:22Z) - Complementing Brightness Constancy with Deep Networks for Optical Flow
Prediction [30.10864927536864]
COMBO is a deep network that exploits the brightness constancy (BC) model used in traditional methods.
We derive a joint training scheme for learning the different components of the decomposition ensuring an optimal cooperation.
Experiments show that COMBO can improve performances over state-of-the-art supervised networks.
arXiv Detail & Related papers (2022-07-08T09:42:40Z) - A Unifying Multi-sampling-ratio CS-MRI Framework With Two-grid-cycle
Correction and Geometric Prior Distillation [7.643154460109723]
We propose a unifying deep unfolding multi-sampling-ratio CS-MRI framework, by merging advantages of model-based and deep learning-based methods.
Inspired by multigrid algorithm, we first embed the CS-MRI-based optimization algorithm into correction-distillation scheme.
We employ a condition module to learn adaptively step-length and noise level from compressive sampling ratio in every stage.
arXiv Detail & Related papers (2022-05-14T13:36:27Z) - DEALIO: Data-Efficient Adversarial Learning for Imitation from
Observation [57.358212277226315]
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator.
Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms.
This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk.
We propose a more data-efficient IfO algorithm
arXiv Detail & Related papers (2021-03-31T23:46:32Z) - A Low Complexity Decentralized Neural Net with Centralized Equivalence
using Layer-wise Learning [49.15799302636519]
We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers)
In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns.
We show that it is possible to achieve equivalent learning performance as if the data is available in a single place.
arXiv Detail & Related papers (2020-09-29T13:08:12Z) - Prototypical Contrastive Learning of Unsupervised Representations [171.3046900127166]
Prototypical Contrastive Learning (PCL) is an unsupervised representation learning method.
PCL implicitly encodes semantic structures of the data into the learned embedding space.
PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks.
arXiv Detail & Related papers (2020-05-11T09:53:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.