2 citations · 3 across the 12 of their papers we have counts for
12 papers
Discovery and Spatial Characterisation of Multiple Shortcut Groups for Auditing Vision Model Bias
Akshit Achara, Vishnunarayan Manickam, Thomas Day +3
Deep learning models trained on datasets with spurious correlations can achieve high average accuracy whilst relying on shortcut features that do not generalise out of distribution…
EquiSteer: Cross-Attention Steering Towards a Fairer Text-Guided Image Generation
Tatiana Gaintseva, Akshit Achara, Gregory Slabaugh +2
Text-to-image diffusion models power everyday creative tasks, but they still reproduce the demographic biases in their training data. On common prompts such as ``a photo of a nurse…
Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift
Akshit Achara, Yovin Yahathugoda, Nick Byrne +4
The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A common way to test for such…
Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast
Mehmet Yigit Avci, Akshit Achara, Andrew King +1
Demographic attributes can be predicted from medical images, raising concerns about bias in clinical AI systems. In X-ray imaging, acquisition characteristics have been shown to co…
Multi-Way Representation Alignment
Akshit Achara, Tatiana Gaintseva, Mateo Mahaut +5
The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces. However, current strategies for mapping t…
Localising Shortcut Learning in Pixel Space via Ordinal Scoring Correlations for Attribution Representations (OSCAR)
Akshit Achara, Peter Triantafillou, Esther Puyol-Antón +2
Deep neural networks often exploit shortcuts. These are spurious cues which are associated with output labels in the training data but are unrelated to task semantics. When the sho…
Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Akshit Achara, Esther Puyol Anton, Alexander Hammers +1
Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's di…
Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers
Akshit Achara, Anshuman Chhabra
AI Safety Moderation (ASM) classifiers are designed to moderate content on social media platforms and to serve as guardrails that prevent Large Language Models (LLMs) from being fi…
Efficient Biomedical Entity Linking: Clinical Text Standardization with Low-Resource Techniques
Akshit Achara, Sanand Sasidharan, Gagan N
Clinical text is rich in information, with mentions of treatment, medication and anatomy among many other clinical terms. Multiple terms can refer to the same core concepts which c…
Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization
Akshit Achara, Ram Krishna Pandey
Supervised deep learning models require significant amount of labeled data to achieve an acceptable performance on a specific task. However, when tested on unseen data, the models…
TrueDeep: A systematic approach of crack detection with less data
Ram Krishna Pandey, Akshit Achara
Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either n…
CoreDeep: Improving Crack Detection Algorithms Using Width Stochasticity
Ram Krishna Pandey, Akshit Achara
Automatically detecting or segmenting cracks in images can help in reducing the cost of maintenance or operations. Detecting, measuring and quantifying cracks for distress analysis…