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Explainable Models

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CPJKU/audioLIME

공식 코드

Towards Musically Meaningful Explanations Using Source Separation

https://github.com/Seth-Park/MultimodalExplanations

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Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

https://github.com/UCDvision/CGC

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Consistent Explanations by Contrastive Learning

https://github.com/aiser-team/cabrnet

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CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models

https://github.com/alien9427/xai4sar-pgil

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Physically Explainable CNN for SAR Image Classification

https://github.com/amayags/biox-cpath

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BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology

https://github.com/ananyajana/interpretable_covid19

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Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data

https://github.com/anirbansarkar-cs/Ante-hoc_Explainability_Concepts

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A Framework for Learning Ante-hoc Explainable Models via Concepts

https://github.com/ashutoshaay26/Relational-Boosted-Bandits

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Relational Boosted Bandits

https://github.com/bbc/cnn-fractional-motion-compensation

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Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding

https://github.com/boniolp/dCAM

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dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification

https://github.com/daniel-ho/SegNBDT

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SegNBDT: Visual Decision Rules for Segmentation

https://github.com/dependable-intelligent-systems-lab/xwhy

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Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations

https://github.com/eBay/xFraud

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xFraud: Explainable Fraud Transaction Detection

https://github.com/edahelsinki/slisemap

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SLISEMAP: Supervised dimensionality reduction through local explanations

https://github.com/giacoballoccu/explanation-quality-recsys

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Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations

https://github.com/goyalpike/RK4_SinDy

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Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach

https://github.com/hiyouga/llama-efficient-tuning

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Large Language Models for Cyber Security: A Systematic Literature Review

https://github.com/holistic-ai/HEARTS-Text-Stereotype-Detection

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HEARTS: A Holistic Framework for Explainable, Sustainable and Robust Text Stereotype Detection

https://github.com/hturbe/protosvit

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ProtoS-ViT: Visual foundation models for sparse self-explainable classifications

https://github.com/jfenogli/xai_boron_la

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Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids

https://github.com/krisrs1128/interpretability_review

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Data Science Principles for Interpretable and Explainable AI

https://github.com/lileipisces/EXTRA

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EXTRA: Explanation Ranking Datasets for Explainable Recommendation

https://github.com/majianthu/aps2020

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Variable Selection with Copula Entropy

https://github.com/mattswatson/attack-agnostic-adversarial-attack-detection

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Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning

https://github.com/maximek3/mimic-nle

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Explaining Chest X-ray Pathologies in Natural Language

https://github.com/mueller-franzes/mst

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Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2

https://github.com/nighttime/entgraph

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Smoothing Entailment Graphs with Language Models

https://github.com/quantumiracle/Cascading-Decision-Tree

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CDT: Cascading Decision Trees for Explainable Reinforcement Learning

https://github.com/robertdvdk/lico-fact

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Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"

https://github.com/runedk93/pantypes

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Pantypes: Diverse Representatives for Self-Explainable Models

https://github.com/samuelkim314/DeepSymReg

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Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

https://github.com/seed-vt/tracefl

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TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance

https://github.com/shreyasipathak/prototype-based-breast-cancer-prediction

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Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges

https://github.com/srigas/KAN_Fault_Diagnosis

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Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks

https://github.com/srishtigautam/kmex

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Prototypical Self-Explainable Models Without Re-training

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