6 research outputs found
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation
Generative adversarial networks (GANs) have achieved remarkable progress in
the natural image field. However, when applying GANs in the remote sensing (RS)
image generation task, an extraordinary phenomenon is observed: the GAN model
is more sensitive to the size of training data for RS image generation than for
natural image generation. In other words, the generation quality of RS images
will change significantly with the number of training categories or samples per
category. In this paper, we first analyze this phenomenon from two kinds of toy
experiments and conclude that the amount of feature information contained in
the GAN model decreases with reduced training data. Then we establish a
structural causal model (SCM) of the data generation process and interpret the
generated data as the counterfactuals. Based on this SCM, we theoretically
prove that the quality of generated images is positively correlated with the
amount of feature information. This provides insights for enriching the feature
information learned by the GAN model during training. Consequently, we propose
two innovative adjustment schemes, namely Uniformity Regularization (UR) and
Entropy Regularization (ER), to increase the information learned by the GAN
model at the distributional and sample levels, respectively. We theoretically
and empirically demonstrate the effectiveness and versatility of our methods.
Extensive experiments on three RS datasets and two natural datasets show that
our methods outperform the well-established models on RS image generation
tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN
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
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
Benefiting from the injection of human prior knowledge, graphs, as derived
discrete data, are semantically dense so that models can efficiently learn the
semantic information from such data. Accordingly, graph neural networks (GNNs)
indeed achieve impressive success in various fields. Revisiting the GNN
learning paradigms, we discover that the relationship between human expertise
and the knowledge modeled by GNNs still confuses researchers. To this end, we
introduce motivating experiments and derive an empirical observation that the
human expertise is gradually learned by the GNNs in general domains. By further
observing the ramifications of introducing expertise logic into graph
representation learning, we conclude that leading the GNNs to learn human
expertise can improve the model performance. By exploring the intrinsic
mechanism behind such observations, we elaborate the Structural Causal Model
for the graph representation learning paradigm. Following the theoretical
guidance, we innovatively introduce the auxiliary causal logic learning
paradigm to improve the model to learn the expertise logic causally related to
the graph representation learning task. In practice, the counterfactual
technique is further performed to tackle the insufficient training issue during
optimization. Plentiful experiments on the crafted and real-world domains
support the consistent effectiveness of the proposed method