15 citations · 25 across the 6 of their papers we have counts for
8 papers
LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction
Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa +3
Dense self-expression matrices and full-affinity spectral clustering limit the scalability of subspace clustering. We introduce the Latent Orthogonal Optimization Model for Subspac…
Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI
Mirza Ahsan Ullah, Tehseen Zia
Skin cancer is one of the most prevalent and potentially life-threatening diseases worldwide, necessitating early and accurate diagnosis to improve patient outcomes. Conventional d…
Leveraging counterfactual concepts for debugging and improving CNN model performance
Syed Ali Tariq, Tehseen Zia
Counterfactual explanation methods have recently received significant attention for explaining CNN-based image classifiers due to their ability to provide easily understandable exp…
Faithful Counterfactual Visual Explanations (FCVE)
Bismillah Khan, Syed Ali Tariq, Tehseen Zia +2
Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI ca…
Towards Counterfactual and Contrastive Explainability and Transparency of DCNN Image Classifiers
Syed Ali Tariq, Tehseen Zia, Mubeen Ghafoor
Explainability of deep convolutional neural networks (DCNNs) is an important research topic that tries to uncover the reasons behind a DCNN model's decisions and improve their unde…
VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics
Ammar A. Siddiqui, Santosh Tirunagari, Tehseen Zia +1
Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image, in contrast to the more common detection of diseased tissue d…
Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks
Tehseen Zia, Zeeshan Nisar, Shakeeb Murtaza
The image-based diagnosis is now a vital aspect of modern automation assisted diagnosis. To enable models to produce pixel-level diagnosis, pixel-level ground-truth labels are esse…
Text-to-Image Generation with Attention Based Recurrent Neural Networks
Tehseen Zia, Shahan Arif, Shakeeb Murtaza +1
Conditional image modeling based on textual descriptions is a relatively new domain in unsupervised learning. Previous approaches use a latent variable model and generative adversa…