Cover Image

Interpretable Diffusion Models with B-cos Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bernold, Nicola, Vandenhirtz, Moritz, Bizeul, Alice, Vogt, Julia E.
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
by: Vandenhirtz, Moritz, et al.
Published: (2025)

Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
by: Goncalves, Jorge da Silva, et al.
Published: (2024)

From Logits to Hierarchies: Hierarchical Clustering made Simple
by: Palumbo, Emanuele, et al.
Published: (2024)

Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks
by: Kleinmann, Marcel, et al.
Published: (2025)

From Pixels to Components: Eigenvector Masking for Visual Representation Learning
by: Bizeul, Alice, et al.
Published: (2025)

TreeDiffusion: Hierarchical Generative Clustering for Conditional Diffusion
by: Gonçalves, Jorge da Silva, et al.
Published: (2024)

Leveraging the Structure of Medical Data for Improved Representation Learning
by: Agostini, Andrea, et al.
Published: (2025)

B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers
by: Böhle, Moritz, et al.
Published: (2023)

Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography
by: Böhi, Simon, et al.
Published: (2026)

Structure is Supervision: Multiview Masked Autoencoders for Radiology
by: Laguna, Sonia, et al.
Published: (2025)

Optimising for Interpretability: Convolutional Dynamic Alignment Networks
by: Böhle, Moritz, et al.
Published: (2021)

B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
by: Arya, Shreyash, et al.
Published: (2024)

Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks
by: Carballo-Castro, Alba, et al.
Published: (2024)

Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models
by: Haas, René, et al.
Published: (2023)

CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models
by: He, Zhenghao, et al.
Published: (2026)

Interpreting the Weight Space of Customized Diffusion Models
by: Dravid, Amil, et al.
Published: (2024)

Interpreting Neurons in Deep Vision Networks with Language Models
by: Bai, Nicholas, et al.
Published: (2024)

A Unified Framework for Diffusion Model Unlearning with f-Divergence
by: Novello, Nicola, et al.
Published: (2025)

Patronus: Interpretable Diffusion Models with Prototypes
by: Weng, Nina, et al.
Published: (2025)

Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
by: Shi, Yingdong, et al.
Published: (2025)

Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
by: Donnelly, Jon, et al.
Published: (2025)

STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising
by: Liu, Jingjing, et al.
Published: (2025)

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features
by: Helbling, Alec, et al.
Published: (2025)

Cluster Paths: Navigating Interpretability in Neural Networks
by: Kroeger, Nicholas M., et al.
Published: (2025)

Investigating the Effect of Network Pruning on Performance and Interpretability
by: von Rad, Jonathan, et al.
Published: (2024)

GAUDA: Generative Adaptive Uncertainty-guided Diffusion-based Augmentation for Surgical Segmentation
by: Frisch, Yannik, et al.
Published: (2025)

Residualized Temporal Sparse Autoencoders for Interpreting Diffusion Models
by: Yeung, Calvin, et al.
Published: (2026)

Interpreting and Improving Diffusion Models from an Optimization Perspective
by: Permenter, Frank, et al.
Published: (2023)

CIP-Net: Continual Interpretable Prototype-based Network
by: Di Valerio, Federico, et al.
Published: (2025)

Neural Network Diffusion
by: Wang, Kai, et al.
Published: (2024)

On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks
by: Plebe, Alice, et al.
Published: (2020)

Manipulating Embeddings of Stable Diffusion Prompts
by: Deckers, Niklas, et al.
Published: (2023)

Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
by: Wang, Bin, et al.
Published: (2022)

Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing
by: Bieder, Florentin, et al.
Published: (2023)

SIC: Similarity-Based Interpretable Image Classification with Neural Networks
by: Wolf, Tom Nuno, et al.
Published: (2025)

Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
by: Hu, Xuran, et al.
Published: (2024)

Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings
by: Chopard, Daphné, et al.
Published: (2024)

EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving
by: Palladino, Vittorio, et al.
Published: (2026)

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task
by: Maleki, Alireza, et al.
Published: (2024)

Origins of Creativity in Attention-Based Diffusion Models
by: Finn, Emma, et al.
Published: (2025)