Faster Projected GAN: Towards Faster Few-Shot Image Generation

2024-01-23 · Chuang Wang, ZhengPing Li, Yuwen Hao, Lijun Wang, Xiaoxue Li

In order to solve the problems of long training time, large consumption of computing resources and huge parameter amount of GAN network in image generation, this paper proposes an improved GAN network model, which is named Faster Projected GAN, based on Projected GAN. The proposed network is mainly focuses on the improvement of generator of Projected GAN. By introducing depth separable convolution (DSC), the number of parameters of the Projected GAN is reduced, the training speed is accelerated, and memory is saved. Experimental results show that on ffhq-1k, art-painting, Landscape and other few-shot image datasets, a 20% speed increase and a 15% memory saving are achieved. At the same time, FID loss is less or no loss, and the amount of model parameters is better controlled. At the same time, significant training speed improvement has been achieved in the small sample image generation task of special scenes such as earthquake scenes with few public datasets.

📄 PDF Abstract BibTeX arXiv:2403.08778

Code (0)

등록된 구현이 없습니다.

Tasks

Image Generation

Methods 이 논문이 사용한 방법론

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

Projected GANs Converge Faster

2021-11-01 · NeurIPS 2021 12 · Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger

Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-parameter sweeps. We make significant headwa…

Conditional Image Generation Image Generation

Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations

2020-09-29 · Pu Zhao, Parikshit Ram, Songtao Lu, Yuguang Yao 외

Adversarial perturbations are critical for certifying the robustness of deep learning models. A universal adversarial perturbation (UAP) can simultaneously attack multiple images, and thus offers a more unified threat mo…

Adversarial Attack Bilevel Optimization Few-Shot Learning Meta-Learning

Matching Free Depth Recovery from Structured Light

2025-01-13 · Zhuohang Yu, Kai Wang, Juyong Zhang

We present a novel approach for depth estimation from images captured by structured light systems. Unlike many previous methods that rely on image matching process, our approach uses a density voxel grid to represent sce…

Depth Estimation

DriftXpress: Faster Drifting Models via Projected RKHS Fields

2026-05-12 · Ali Falahati, Elliot Creager, Gautam Kamath, Shubhankar Mohapatra arxiv

Drifting Models have emerged as a new paradigm for one-step generative modeling, achieving strong image quality without iterative inference. The premise is to replace the iterative denoising process in diffusion models w…

Real-Time Seamless Single Shot 6D Object Pose Prediction

2017-11-24 · CVPR 2018 6 · Bugra Tekin, Sudipta N. Sinha, Pascal Fua

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed s…

6D Pose Estimation using RGB Drone Pose Estimation GPU Object+3