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20202026
most citedTowards Counterfactual and Contrastive Explainability and Transparency of DCNN Image Classifiers

15 citations · 25 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

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…

cs.CV2025★ 2 cited

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025★ 15 cited

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…

cs.CV2023

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…

cs.CV2020★ 6 cited

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…