167 citations · 1k across the 73 of their papers we have counts for
74 papers
Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning
Hubert Baniecki, Przemyslaw Biecek
A common belief is that intrinsically interpretable deep learning models ensure a correct, intuitive understanding of their behavior and offer greater robustness against accidental…
Interpreting CLIP with Hierarchical Sparse Autoencoders
Vladimir Zaigrajew, Hubert Baniecki, Przemyslaw Biecek
Sparse autoencoders (SAEs) are useful for detecting and steering interpretable features in neural networks, with particular potential for understanding complex multimodal represent…
Position: Do Not Explain Vision Models Without Context
Paulina Tomaszewska, Przemysław Biecek
Does the stethoscope in the picture make the adjacent person a doctor or a patient? This, of course, depends on the contextual relationship of the two objects. If it's obvious, why…
Does context matter in digital pathology?
Paulina Tomaszewska, Mateusz Sperkowski, Przemysław Biecek
The development of Artificial Intelligence for healthcare is of great importance. Models can sometimes achieve even superior performance to human experts, however, they can reason…
CNN-based explanation ensembling for dataset, representation and explanations evaluation
Weronika Hryniewska-Guzik, Luca Longo, Przemysław Biecek
Explainable Artificial Intelligence has gained significant attention due to the widespread use of complex deep learning models in high-stake domains such as medicine, finance, and…
Red-Teaming Segment Anything Model
Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski +4
Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment…