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Explainable Models

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코드

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

https://github.com/theabrusch/freqrise

공식 코드

FreqRISE: Explaining time series using frequency masking

https://github.com/tissueimageanalytics/cerberus

공식 코드

One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification

https://github.com/trustinai/gdvaecode

공식 코드

The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations

https://github.com/ukp-square/square-core

공식 코드

UKP-SQUARE: An Online Platform for Question Answering Research

https://github.com/vadel/ae4xai

공식 코드

When and How to Fool Explainable Models (and Humans) with Adversarial Examples

https://github.com/visinf/idsds

공식 코드

Benchmarking the Attribution Quality of Vision Models

https://github.com/vladhondru25/exddv

공식 코드

ExDDV: A New Dataset for Explainable Deepfake Detection in Video

https://github.com/warisgill/tracefl

공식 코드

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

https://github.com/wei2624/gpnam

공식 코드

Gaussian Process Neural Additive Models

https://github.com/xrt11/xai-code

공식 코드

Accurate Explanation Model for Image Classifiers using Class Association Embedding

https://github.com/yaorong0921/hxai-survey

공식 코드

Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations

https://github.com/Imageomics/Prompt_CAM

Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis

https://github.com/YoruCathy/USDSeg-FCOS

Learning Universal Shape Dictionary for Realtime Instance Segmentation

https://github.com/mpimd-csc/RK4-SinDy

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

https://github.com/shuheng-li/units-sensory-time-series-classification

Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.