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Taslim Mahbub

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3 papers

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AAAI Conference 2026 Conference Paper

Mitigating Self-Preference by Authorship Obfuscation

Taslim Mahbub
Shi Feng

Language models (LMs) judges are widely used to evaluate the quality of LM outputs. Despite many advantages, LM judges display concerning biases that can impair their integrity in evaluations. One such bias is self-preference: LM judges preferring their own answers over those produced by other LMs or humans. The bias is hard to eliminate as frontier LM judges can distinguish their own outputs from those of others, even when the evaluation candidates are not labeled with their sources. In this paper, we investigate strategies to mitigate self-preference by reducing the LM judges' ability to recognize their own outputs. We apply black-box perturbations to evaluation candidates in pairwise comparison to obfuscate the authorship and reduce self-recognition. We find that perturbations as simple as synonym replacement for a few words predictably reduce self-preference. However, we also uncover fundamental challenges to eliminating the bias: when we extrapolate our perturbations to a more complete neutralization of stylistic differences between the evaluation candidates, self-preference recovers. Our findings suggest that self-recognition and self-preference can happen on many semantic levels, and complete mitigation remains challenging despite promising initial results.

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JBHI Journal 2024 Journal Article

Center-Focused Affinity Loss for Class Imbalance Histology Image Classification

Taslim Mahbub
Ahmad Obeid
Sajid Javed
Jorge Dias
Taimur Hassan
Naoufel Werghi

Early-stage cancer diagnosis potentially improves the chances of survival for many cancer patients worldwide. Manual examination of Whole Slide Images (WSIs) is a time-consuming task for analyzing tumor-microenvironment. To overcome this limitation, the conjunction of deep learning with computational pathology has been proposed to assist pathologists in efficiently prognosing the cancerous spread. Nevertheless, the existing deep learning methods are ill-equipped to handle fine-grained histopathology datasets. This is because these models are constrained via conventional softmax loss function, which cannot expose them to learn distinct representational embeddings of the similarly textured WSIs containing an imbalanced data distribution. To address this problem, we propose a novel center-focused affinity loss (CFAL) function that exhibits 1) constructing uniformly distributed class prototypes in the feature space, 2) penalizing difficult samples, 3) minimizing intra-class variations, and 4) placing greater emphasis on learning minority class features. We evaluated the performance of the proposed CFAL loss function on two publicly available breast and colon cancer datasets having varying levels of imbalanced classes. The proposed CFAL function shows better discrimination abilities as compared to the popular loss functions such as ArcFace, CosFace, and Focal loss. Moreover, it outperforms several SOTA methods for histology image classification across both datasets.

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EAAI Journal 2023 Journal Article

Window-based transformer generative adversarial network for autonomous underwater image enhancement

Mehnaz Ummar
Fayaz Ali Dharejo
Basit Alawode
Taslim Mahbub
Md. Jalil Piran
Sajid Javed

The estimation of high-quality underwater images is an important step towards the development of computer vision systems in marine environments. This fundamental step contains numerous computer vision and robotics applications including marine exploration, robotics manipulation, navigation, object detection, tracking, and sea life monitoring. However, this pre-processing step becomes more challenging in the presence of back-scattering of underwater particles and attenuation issues, which lead to the formation of hazy underwater images. Vision transformers have recently demonstrated outstanding performance in many computer vision applications. Window-based Transformers (WT) show promising enhancement performance by computing self-attention within non-overlapping local windows. WT has been identified as an essential component in improving representation capabilities; however, it has received less attention in improving the performance of Underwater Image Enhancement (UIE). Therefore, we propose a novel end-to-end Underwater window-based Transformer Generative Adversarial Network (UwTGAN). Our proposed algorithm consists of two main components, including a transformer generator that generates a restored underwater image and a transformer discriminator that classifies the generated underwater image. Both components are equipped with Window-based Self-Attention Blocks (WSABs), which maximize efficiency by limiting self-attention computation to non-overlapping local windows and provide relatively low computational costs. WSAB-based transformer generators and discriminators are trained end-to-end. We formulated an efficient loss function to ensure that the variables are closely integrated. Extensive experimental evaluations are performed on four independent underwater image datasets. Our results demonstrate that the proposed UwTGAN algorithm has outperformed several state-of-the-art UIE methods in terms of both quantitative and qualitative metrics by a significant margin.

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