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Found 12 papers, 9 papers with code
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pre-training-free learning-based algorithm for solving the BSR problem.
Worst-Case Riemannian Optimization with Uncertain Target Steering Vector for Slow-Time Transmit Sequence of Cognitive Radar
In order to solve this problem, we propose a new optimization method for slow-time transmit sequence design.
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
While a considerable amount of research has been dedicated to remote sensing classification, object detection and semantic segmentation, most of these studies have overlooked the valuable prior knowledge embedded within remote sensing scenarios.
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning
For this reason, this paper explores a Source-Free CDFSL (SF-CDFSL) problem, in which CDFSL is addressed through the use of existing pretrained models instead of training a model with source data, avoiding accessing source data.
Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR
Recently, there has been increasing concern about the vulnerability of deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) to adversarial attacks, where a DNN could be easily deceived by clean input with imperceptible but aggressive perturbations.
Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture
Besides, we employ local masks and multi-scale features to accommodate the various small targets in remote sensing.
Enhancing Representations through Heterogeneous Self-Supervised Learning
Thus, we propose Heterogeneous Self-Supervised Learning (HSSL), which enforces a base model to learn from an auxiliary head whose architecture is heterogeneous from the base model.
Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection
A standard convex SPCA-based model with PSD constraint for unsupervised feature selection is proposed.
Toward Adversarial Training on Contextualized Language Representation
Based on the observation, we propose simple yet effective \textit{Contextualized representation-Adversarial Training} (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder.
Deep Intellectual Property Protection: A Survey
Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields.
Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition
However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations.
Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR
Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR.
