MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Bor-Shiun, Wang, Chien-Yi, Chiu, Wei-Chen |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
by: Donnelly, Jon, et al.
Published: (2021)
by: Donnelly, Jon, et al.
Published: (2021)
A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations
by: Saralajew, Sascha, et al.
Published: (2024)
by: Saralajew, Sascha, et al.
Published: (2024)
From Segments to Concepts: Interpretable Image Classification via Concept-Guided Segmentation
by: Eisenberg, Ran, et al.
Published: (2025)
by: Eisenberg, Ran, et al.
Published: (2025)
On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis
by: Davoodi, Omid, et al.
Published: (2023)
by: Davoodi, Omid, et al.
Published: (2023)
Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
by: Lv, Xueqiang, et al.
Published: (2026)
by: Lv, Xueqiang, et al.
Published: (2026)
Enhancing Pre-trained Representation Classifiability can Boost its Interpretability
by: Shen, Shufan, et al.
Published: (2025)
by: Shen, Shufan, et al.
Published: (2025)
Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
by: Kulkarni, Akshay, et al.
Published: (2025)
by: Kulkarni, Akshay, et al.
Published: (2025)
IPEC: Test-Time Incremental Prototype Enhancement Classifier for Few-Shot Learning
by: Liao, Wenwen, et al.
Published: (2026)
by: Liao, Wenwen, et al.
Published: (2026)
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
by: Xu, Xinyue, et al.
Published: (2024)
by: Xu, Xinyue, et al.
Published: (2024)
CIP-Net: Continual Interpretable Prototype-based Network
by: Di Valerio, Federico, et al.
Published: (2025)
by: Di Valerio, Federico, et al.
Published: (2025)
CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models
by: He, Zhenghao, et al.
Published: (2026)
by: He, Zhenghao, et al.
Published: (2026)
Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data
by: Liang, Chao, et al.
Published: (2025)
by: Liang, Chao, et al.
Published: (2025)
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
by: Donnelly, Jon, et al.
Published: (2025)
by: Donnelly, Jon, et al.
Published: (2025)
Interpretable Generative Models through Post-hoc Concept Bottlenecks
by: Kulkarni, Akshay, et al.
Published: (2025)
by: Kulkarni, Akshay, et al.
Published: (2025)
Neural Field Classifiers via Target Encoding and Classification Loss
by: Yang, Xindi, et al.
Published: (2024)
by: Yang, Xindi, et al.
Published: (2024)
VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set
by: Shen, Shufan, et al.
Published: (2025)
by: Shen, Shufan, et al.
Published: (2025)
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
by: Javadi, Amirhosein, et al.
Published: (2026)
by: Javadi, Amirhosein, et al.
Published: (2026)
ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
by: Lin, Shen, et al.
Published: (2026)
by: Lin, Shen, et al.
Published: (2026)
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
by: Wu, Yongliang, et al.
Published: (2024)
by: Wu, Yongliang, et al.
Published: (2024)
Patronus: Interpretable Diffusion Models with Prototypes
by: Weng, Nina, et al.
Published: (2025)
by: Weng, Nina, et al.
Published: (2025)
Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking
by: Deng, Kaiyuan, et al.
Published: (2026)
by: Deng, Kaiyuan, et al.
Published: (2026)
Concept-Guided Interpretability via Neural Chunking
by: Wu, Shuchen, et al.
Published: (2025)
by: Wu, Shuchen, et al.
Published: (2025)
Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification
by: Nguyen, Nghia, et al.
Published: (2026)
by: Nguyen, Nghia, et al.
Published: (2026)
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs
by: Xu, Kaiwen, et al.
Published: (2023)
by: Xu, Kaiwen, et al.
Published: (2023)
Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
by: Sun, Wenju, et al.
Published: (2022)
by: Sun, Wenju, et al.
Published: (2022)
Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification
by: Chen, Yangmei, et al.
Published: (2026)
by: Chen, Yangmei, et al.
Published: (2026)
An Overview of Prototype Formulations for Interpretable Deep Learning
by: Li, Maximilian Xiling, et al.
Published: (2024)
by: Li, Maximilian Xiling, et al.
Published: (2024)
Concept-Centric Token Interpretation for Vector-Quantized Generative Models
by: Yang, Tianze, et al.
Published: (2025)
by: Yang, Tianze, et al.
Published: (2025)
ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features
by: Helbling, Alec, et al.
Published: (2025)
by: Helbling, Alec, et al.
Published: (2025)
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
by: Thasarathan, Harrish, et al.
Published: (2025)
by: Thasarathan, Harrish, et al.
Published: (2025)
ConceptMix++: Leveling the Playing Field in Text-to-Image Benchmarking via Iterative Prompt Optimization
by: Gan, Haosheng, et al.
Published: (2025)
by: Gan, Haosheng, et al.
Published: (2025)
Concepts or Skills? Rethinking Instruction Selection for Multi-modal Models
by: Bai, Andrew, et al.
Published: (2025)
by: Bai, Andrew, et al.
Published: (2025)
WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance
by: Maire, Phillip, et al.
Published: (2025)
by: Maire, Phillip, et al.
Published: (2025)
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
by: Wang, Lei, et al.
Published: (2024)
by: Wang, Lei, et al.
Published: (2024)
MCM: Multi-layer Concept Map for Efficient Concept Learning from Masked Images
by: Sun, Yuwei, et al.
Published: (2025)
by: Sun, Yuwei, et al.
Published: (2025)
Interpreting and Controlling Model Behavior via Constitutions for Atomic Concept Edits
by: Kalibhat, Neha, et al.
Published: (2026)
by: Kalibhat, Neha, et al.
Published: (2026)
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)
by: Bhalla, Usha, et al.
Published: (2024)
by: Bhalla, Usha, et al.
Published: (2024)
Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
by: Tong, Schrasing, et al.
Published: (2026)
by: Tong, Schrasing, et al.
Published: (2026)
Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
by: Li, Kun, et al.
Published: (2024)
by: Li, Kun, et al.
Published: (2024)
Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings
by: Hong, Jinyung, et al.
Published: (2024)
by: Hong, Jinyung, et al.
Published: (2024)
Similar Items
-
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
by: Donnelly, Jon, et al.
Published: (2021) -
A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations
by: Saralajew, Sascha, et al.
Published: (2024) -
From Segments to Concepts: Interpretable Image Classification via Concept-Guided Segmentation
by: Eisenberg, Ran, et al.
Published: (2025) -
On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis
by: Davoodi, Omid, et al.
Published: (2023) -
Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
by: Lv, Xueqiang, et al.
Published: (2026)