Search Results for author:
Found 7 papers, 1 papers with code
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection
Adversarial attacks (AAs) pose a significant threat to the reliability and robustness of deep neural networks.
GCPV: Guided Concept Projection Vectors for the Explainable Inspection of CNN Feature Spaces
The latter, though, is of particular interest for debugging, like finding and understanding outliers, learned notions of sub-concepts, and concept confusion.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces
These allow insights into both the flow and likeness of semantic information within CNN layers, and into the degree of their similarity between different network architectures.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability
The guiding use-case is a post-hoc explainability framework for object detection (OD) CNNs, towards which existing concept analysis (CA) methods are successfully adapted.
Dimensionality Reduction
Explainable artificial intelligence
+4
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing
To address this problem, in this paper we introduce a novel unsupervised cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS.
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing
The proposed CHNR includes two training phases: i) meta-learning phase that uses a small portion of clean (i. e., reliable) data to train the noise detection module in an adversarial fashion; and ii) the main training phase for which the trained noise detection module is used to identify noisy correspondences while the hashing module is trained on the noisy multi-modal training set.
Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing
To address this problem, in this paper we introduce a novel deep unsupervised cross-modal contrastive hashing (DUCH) method for RS text-image retrieval.
