A Unified GAN Framework Regarding Manifold Alignment for Remote Sensing Images Generation

Generative Adversarial Networks (GANs) and their variants have achieved remarkable success on natural images. However, their performance degrades when applied to remote sensing (RS) images, and the discriminator often suffers from the overfitting problem. In this paper, we examine the differences between natural and RS images and find that the intrinsic dimensions of RS images are much lower than those of natural images. As the discriminator is more susceptible to overfitting on data with lower intrinsic dimension, it focuses excessively on local characteristics of RS training data and disregards the overall structure of the distribution, leading to a faulty generation model. In respond, we propose a novel approach that leverages the real data manifold to constrain the discriminator and enhance the model performance. Specifically, we introduce a learnable information-theoretic measure to capture the real data manifold. Building upon this measure, we propose manifold alignment regularization, which mitigates the discriminator's overfitting and improves the quality of generated samples. Moreover, we establish a unified GAN framework for manifold alignment, applicable to both supervised and unsupervised RS image generation tasks

Learning to Sample Tasks for Meta Learning

Through experiments on various meta-learning methods, task samplers, and few-shot learning tasks, this paper arrives at three conclusions. Firstly, there are no universal task sampling strategies to guarantee the performance of meta-learning models. Secondly, task diversity can cause the models to either underfit or overfit during training. Lastly, the generalization performance of the models are influenced by task divergence, task entropy, and task difficulty. In response to these findings, we propose a novel task sampler called Adaptive Sampler (ASr). ASr is a plug-and-play task sampler that takes task divergence, task entropy, and task difficulty to sample tasks. To optimize ASr, we rethink and propose a simple and general meta-learning algorithm. Finally, a large number of empirical experiments demonstrate the effectiveness of the proposed ASr.Comment: 10 pages, 7 tables, 3 figure

Unbiased Image Synthesis via Manifold-Driven Sampling in Diffusion Models

Diffusion models are a potent class of generative models capable of producing high-quality images. However, they can face challenges related to data bias, favoring specific modes of data, especially when the training data does not accurately represent the true data distribution and exhibits skewed or imbalanced patterns. For instance, the CelebA dataset contains more female images than male images, leading to biased generation results and impacting downstream applications. To address this issue, we propose a novel method that leverages manifold guidance to mitigate data bias in diffusion models. Our key idea is to estimate the manifold of the training data using an unsupervised approach, and then use it to guide the sampling process of diffusion models. This encourages the generated images to be uniformly distributed on the data manifold without altering the model architecture or necessitating labels or retraining. Theoretical analysis and empirical evidence demonstrate the effectiveness of our method in improving the quality and unbiasedness of image generation compared to standard diffusion models