Search Results for author:
Found 8 papers, 0 papers with code
Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models
Image cartoonization has attracted significant interest in the field of image generation.
Background Debiased SAR Target Recognition via Causal Interventional Regularizer
Based on the constructed SCM, we propose a causal intervention based regularization method to eliminate the negative impact of background on feature semantic learning and achieve background debiased SAR-ATR.
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning
To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks.
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images
To handle this problem, in this paper, learning transferrable representations from unlabeled PolSAR data through convolutional architectures is explored for the first time.
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification
By DAS, the weights parameters and architecture parameters (corresponds to the hyperparameters but not the topologies) can be optimized by stochastic gradient descent method during the training.
Band Attention Convolutional Networks For Hyperspectral Image Classification
Unlike most of deep learning methods used in HSIs, the band attention module which is customized according to the characteristics of hyperspectral images is embedded in the ordinary CNNs for better performance.
Kernel based regression with robust loss function via iteratively reweighted least squares
Least squares kernel based methods have been widely used in regression problems due to the simple implementation and good generalization performance.
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework
To solve the above problem, the objective of this paper is to develop a tailored CNN framework for PolSAR image classification, which can be implemented from two aspects: Seeking a better form of PolSAR data as the input of CNNs and building matched CNN architectures based on the proposed input form.
