Copertina

B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Arya, Shreyash, Rao, Sukrut, Böhle, Moritz, Schiele, Bernt
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Better Understanding Differences in Attribution Methods via Systematic Evaluations
di: Rao, Sukrut, et al.
Pubblicazione: (2023)

Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery
di: Rao, Sukrut, et al.
Pubblicazione: (2024)

Good Teachers Explain: Explanation-Enhanced Knowledge Distillation
di: Parchami-Araghi, Amin, et al.
Pubblicazione: (2024)

Studying How to Efficiently and Effectively Guide Models with Explanations
di: Rao, Sukrut, et al.
Pubblicazione: (2023)

Towards Better Understanding Attribution Methods
di: Rao, Sukrut, et al.
Pubblicazione: (2022)

FaCT: Faithful Concept Traces for Explaining Neural Network Decisions
di: Parchami-Araghi, Amin, et al.
Pubblicazione: (2025)

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

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

CFM: Language-aligned Concept Foundation Model for Vision
di: Wittenmayer, Kai, et al.
Pubblicazione: (2026)

Adversarial Training against Location-Optimized Adversarial Patches
di: Rao, Sukrut, et al.
Pubblicazione: (2020)

Pixel-level Certified Explanations via Randomized Smoothing
di: Anani, Alaa, et al.
Pubblicazione: (2025)

Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
di: Arya, Shivvrat, et al.
Pubblicazione: (2024)

ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography
di: Ghamary, Yeganeh, et al.
Pubblicazione: (2025)

Identifying Bias in Deep Neural Networks Using Image Transforms
di: Erukude, Sai Teja, et al.
Pubblicazione: (2024)

An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
di: Chen, Hao, et al.
Pubblicazione: (2022)

How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations
di: Gairola, Siddhartha, et al.
Pubblicazione: (2025)

PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding
di: Kuzucu, Selim, et al.
Pubblicazione: (2026)

Evaluation and Analysis of Deep Neural Transformers and Convolutional Neural Networks on Modern Remote Sensing Datasets
di: Hurt, J. Alex, et al.
Pubblicazione: (2025)

DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
di: Wróbel, Adam, et al.
Pubblicazione: (2026)

AIM: Amending Inherent Interpretability via Self-Supervised Masking
di: Alshami, Eyad, et al.
Pubblicazione: (2025)

Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance
di: Bağcı, Doğukan, et al.
Pubblicazione: (2026)

ECG-IMN: Interpretable Mesomorphic Neural Networks for 12-Lead Electrocardiogram Interpretation
di: Thambawita, Vajira, et al.
Pubblicazione: (2026)

Unlearning-based Neural Interpretations
di: Choi, Ching Lam, et al.
Pubblicazione: (2024)

Improving Interpretation Faithfulness for Vision Transformers
di: Hu, Lijie, et al.
Pubblicazione: (2023)

Financial Fraud Identification and Interpretability Study for Listed Companies Based on Convolutional Neural Network
di: Li, Xiao
Pubblicazione: (2025)

Convolutional Dynamic Alignment Networks for Interpretable Classifications
di: Böhle, Moritz, et al.
Pubblicazione: (2021)

Deep Convolutional Neural Networks for Palm Fruit Maturity Classification
di: Han, Mingqiang, et al.
Pubblicazione: (2025)

An Overview of Prototype Formulations for Interpretable Deep Learning
di: Li, Maximilian Xiling, et al.
Pubblicazione: (2024)

Understanding Multimodal Deep Neural Networks: A Concept Selection View
di: Shang, Chenming, et al.
Pubblicazione: (2024)

Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
di: Yamaguchi, Shin'ya, et al.
Pubblicazione: (2024)

Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision
di: Chang, Wonjoon, et al.
Pubblicazione: (2023)

VeLU: Variance-enhanced Learning Unit for Deep Neural Networks
di: Shakarami, Ashkan, et al.
Pubblicazione: (2025)

Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations
di: Ahmadi, Seyed Mohammad, et al.
Pubblicazione: (2024)

Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm
di: Labib, S. M. Fazle Rabby, et al.
Pubblicazione: (2023)

Concept-Guided Interpretability via Neural Chunking
di: Wu, Shuchen, et al.
Pubblicazione: (2025)

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data
di: Wisniewski, Mariusz, et al.
Pubblicazione: (2024)

Harden Deep Neural Networks Against Fault Injections Through Weight Scaling
di: Fuengfusin, Ninnart, et al.
Pubblicazione: (2024)

Building Optimal Neural Architectures using Interpretable Knowledge
di: Mills, Keith G., et al.
Pubblicazione: (2024)

Vertical LoRA: Dense Expectation-Maximization Interpretation of Transformers
di: Fu, Zhuolin
Pubblicazione: (2024)

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
di: Schmalwasser, Laines, et al.
Pubblicazione: (2025)