OMENN: One Matrix to Explain Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Wróbel, Adam, Janusz, Mikołaj, Zieliński, Bartosz, Rymarczyk, Dawid |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification
by: Janusz, Mikołaj, et al.
Published: (2026)
by: Janusz, Mikołaj, et al.
Published: (2026)
Revisiting FunnyBirds evaluation framework for prototypical parts networks
by: Opłatek, Szymon, et al.
Published: (2024)
by: Opłatek, Szymon, et al.
Published: (2024)
DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
by: Wróbel, Adam, et al.
Published: (2026)
by: Wróbel, Adam, et al.
Published: (2026)
LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision
by: Pach, Mateusz, et al.
Published: (2024)
by: Pach, Mateusz, et al.
Published: (2024)
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
by: Olszewski, Jan, et al.
Published: (2023)
by: Olszewski, Jan, et al.
Published: (2023)
SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
by: Dubovik, Viktar, et al.
Published: (2025)
by: Dubovik, Viktar, et al.
Published: (2025)
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
by: Sacha, Mikołaj, et al.
Published: (2023)
by: Sacha, Mikołaj, et al.
Published: (2023)
Explaining Bayesian Neural Networks
by: Bykov, Kirill, et al.
Published: (2021)
by: Bykov, Kirill, et al.
Published: (2021)
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
by: Michalski, Tomasz, et al.
Published: (2025)
by: Michalski, Tomasz, et al.
Published: (2025)
InfoDisent: Explainability of Image Classification Models by Information Disentanglement
by: Struski, Łukasz, et al.
Published: (2024)
by: Struski, Łukasz, et al.
Published: (2024)
One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
by: Małkiński, Mikołaj, et al.
Published: (2023)
by: Małkiński, Mikołaj, et al.
Published: (2023)
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
by: Struski, Łukasz, et al.
Published: (2023)
by: Struski, Łukasz, et al.
Published: (2023)
FaCT: Faithful Concept Traces for Explaining Neural Network Decisions
by: Parchami-Araghi, Amin, et al.
Published: (2025)
by: Parchami-Araghi, Amin, et al.
Published: (2025)
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
by: Schmalwasser, Laines, et al.
Published: (2025)
by: Schmalwasser, Laines, et al.
Published: (2025)
A Unified View of Abstract Visual Reasoning Problems
by: Małkiński, Mikołaj, et al.
Published: (2024)
by: Małkiński, Mikołaj, et al.
Published: (2024)
A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning
by: Małkiński, Mikołaj, et al.
Published: (2024)
by: Małkiński, Mikołaj, et al.
Published: (2024)
Multi-Label Contrastive Learning for Abstract Visual Reasoning
by: Małkiński, Mikołaj, et al.
Published: (2020)
by: Małkiński, Mikołaj, et al.
Published: (2020)
Advancing Generalization Across a Variety of Abstract Visual Reasoning Tasks
by: Małkiński, Mikołaj, et al.
Published: (2025)
by: Małkiński, Mikołaj, et al.
Published: (2025)
Your CLIP has 164 dimensions of noise: Exploring the embeddings covariance eigenspectrum of contrastively pretrained vision-language transformers
by: Grzywaczewski, Jakub, et al.
Published: (2026)
by: Grzywaczewski, Jakub, et al.
Published: (2026)
AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients
by: Sroka-Oleksiak, Agnieszka, et al.
Published: (2025)
by: Sroka-Oleksiak, Agnieszka, et al.
Published: (2025)
Reasoning Limitations of Multimodal Large Language Models. A Case Study of Bongard Problems
by: Małkiński, Mikołaj, et al.
Published: (2024)
by: Małkiński, Mikołaj, et al.
Published: (2024)
Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems
by: Pawlonka, Szymon, et al.
Published: (2025)
by: Pawlonka, Szymon, et al.
Published: (2025)
SwordBench: Evaluating Orthogonality of Steering Image Representations
by: Zaigrajew, Vladimir, et al.
Published: (2026)
by: Zaigrajew, Vladimir, et al.
Published: (2026)
From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks
by: Lee, Jae Hee, et al.
Published: (2023)
by: Lee, Jae Hee, et al.
Published: (2023)
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
by: Hryniewska-Guzik, Weronika, et al.
Published: (2024)
by: Hryniewska-Guzik, Weronika, et al.
Published: (2024)
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
by: Ntrougkas, Mariano V., et al.
Published: (2024)
by: Ntrougkas, Mariano V., et al.
Published: (2024)
Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions
by: Gannamaneni, Sujan Sai, et al.
Published: (2025)
by: Gannamaneni, Sujan Sai, et al.
Published: (2025)
Can Biases in ImageNet Models Explain Generalization?
by: Gavrikov, Paul, et al.
Published: (2024)
by: Gavrikov, Paul, et al.
Published: (2024)
Good Teachers Explain: Explanation-Enhanced Knowledge Distillation
by: Parchami-Araghi, Amin, et al.
Published: (2024)
by: Parchami-Araghi, Amin, et al.
Published: (2024)
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning
by: Yaras, Can, et al.
Published: (2024)
by: Yaras, Can, et al.
Published: (2024)
Position: Do Not Explain Vision Models Without Context
by: Tomaszewska, Paulina, et al.
Published: (2024)
by: Tomaszewska, Paulina, et al.
Published: (2024)
Explaining Model Overfitting in CNNs via GMM Clustering
by: Dou, Hui, et al.
Published: (2024)
by: Dou, Hui, et al.
Published: (2024)
Token Activation Map to Visually Explain Multimodal LLMs
by: Li, Yi, et al.
Published: (2025)
by: Li, Yi, et al.
Published: (2025)
TextCAM: Explaining Class Activation Map with Text
by: Zhao, Qiming, et al.
Published: (2025)
by: Zhao, Qiming, et al.
Published: (2025)
Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment
by: Ashrafee, Alif, et al.
Published: (2025)
by: Ashrafee, Alif, et al.
Published: (2025)
STEP-Parts: Geometric Partitioning of Boundary Representations for Large-Scale CAD Processing
by: Fan, Shen, et al.
Published: (2026)
by: Fan, Shen, et al.
Published: (2026)
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process
by: Adhane, Gereziher, et al.
Published: (2024)
by: Adhane, Gereziher, et al.
Published: (2024)
Explaining Low Perception Model Competency with High-Competency Counterfactuals
by: Pohland, Sara, et al.
Published: (2025)
by: Pohland, Sara, et al.
Published: (2025)
Explaining Generalization Power of a DNN Using Interactive Concepts
by: Zhou, Huilin, et al.
Published: (2023)
by: Zhou, Huilin, et al.
Published: (2023)
Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions
by: Baniecki, Hubert, et al.
Published: (2025)
by: Baniecki, Hubert, et al.
Published: (2025)
Similar Items
-
ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification
by: Janusz, Mikołaj, et al.
Published: (2026) -
Revisiting FunnyBirds evaluation framework for prototypical parts networks
by: Opłatek, Szymon, et al.
Published: (2024) -
DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
by: Wróbel, Adam, et al.
Published: (2026) -
LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision
by: Pach, Mateusz, et al.
Published: (2024) -
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
by: Olszewski, Jan, et al.
Published: (2023)