Papers with Code 아카이브
Image Attribution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 24
Image attribution algorithms aim to identify important regions that are highly relevant to model decisions.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/castorini/daam
공식 코드What the DAAM: Interpreting Stable Diffusion Using Cross Attention
https://github.com/eclique/RISE
공식 코드RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/ictmcg/dna-det
공식 코드https://github.com/k8xu/imageattribution
공식 코드Detecting Origin Attribution for Text-to-Image Diffusion Models
https://github.com/lydialy8/openset_attribution_synthetic_images
공식 코드A Siamese-based Verification System for Open-set Architecture Attribution of Synthetic Images
https://github.com/marcotcr/lime-experiments
공식 코드"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/mit-ll/lorax_cil
공식 코드LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
https://github.com/mlmed/gifsplanation
공식 코드https://github.com/ningyu1991/GANFingerprints
공식 코드Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints
https://github.com/paulnovello/hsic-attribution-method
공식 코드Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
https://github.com/ramprs/grad-cam
공식 코드Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ruoyuchen10/smdl-attribution
공식 코드Less is More: Fewer Interpretable Region via Submodular Subset Selection
https://github.com/tubui/image_attribution
공식 코드RepMix: Representation Mixing for Robust Attribution of Synthesized Images
https://github.com/vishal3477/Reverse_Engineering_GMs
공식 코드Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
https://github.com/632652101/VisualizeCNN-Pd
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/AhnYoungBin/Grad_cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlanYangYi/GayPhotoStyleRecongnizer
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlanYangYi/PhotoStyleRecongnizer
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AldrichCabrera/gradCam-smallvgg
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlejandroAttento/Pytorch-Captum
https://github.com/AlexIoannides/lime-interpretable-ml
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/AlexSzen/wwf_shark_id
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Ankush96/grad-cam.tensorflow
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Cherrydomini/Effects-of-feature-dropping-on-COMPAS-with-the-influence-of-LIME-
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/Cloud-CV/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Deepak3994/Grad-Cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Deepak3994/grad-cam-editted
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Eljefemasao/Customed-Object-Detector
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Eljefemasao/MarkNet-ObjectDetector
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Fragjacker/Pointcloud-grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/FrancescoSaverioZuppichini/A-journey-into-Convolutional-Neural-Network-visualization-
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/GISH123/Cathay-Holdings-CIP-Projects-for-Interpretable-Machine-Learning
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/Hydragon516/Grad-CAM-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/IS2AI/Chest-X-ray-module
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.