6 papers · 1 filter
Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot
Jorge Chang Ortega, Bastien Le Lan, Thomas Serre +1
A central question in computational vision is whether human-like visual representations are better explained by discriminative or generative learning. Existing comparisons, however…
Object-Level Explanations for Image Geolocation Models: a GeoGuessr use-case
Emilie Durrieu, Christophe Hurter, Philippe Muller +1
When humans play geolocation games such as GeoGuessr, they rely on concrete visual cues, such as road markings, vegetation, or architectural details, to infer where an image was ca…
Cross-Modal Redundancy and the Geometry of Vision-Language Embeddings
Grégoire Dhimoïla, Thomas Fel, Victor Boutin +1
Vision-language models (VLMs) align images and text with remarkable success, yet the geometry of their shared embedding space remains poorly understood. To probe this geometry, we…
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
Thomas Fel, Ekdeep Singh Lubana, Jacob S. Prince +7
Sparse Autoencoders (SAEs) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dicti…
Choosing the right basis for interpretability: Psychophysical comparison between neuron-based and dictionary-based representations
Julien Colin, Lore Goetschalckx, Thomas Fel +3
Interpretability research often adopts a neuron-centric lens, treating individual neurons as the fundamental units of explanation. However, neuron-level explanations can be undermi…
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks
Victor Boutin, Rishav Mukherji, Aditya Agrawal +4
Humans can effortlessly draw new categories from a single exemplar, a feat that has long posed a challenge for generative models. However, this gap has started to close with recent…