Kim et al., 2021 - Google Patents
Sanity simulations for saliency methodsKim et al., 2021
View PDF- Document ID
- 7944058318921349973
- Author
- Kim J
- Plumb G
- Talwalkar A
- Publication year
- Publication venue
- arXiv preprint arXiv:2105.06506
External Links
Snippet
Saliency methods are a popular class of feature attribution explanation methods that aim to capture a model's predictive reasoning by identifying" important" pixels in an input image. However, the development and adoption of these methods are hindered by the lack of …
- 238000011156 evaluation 0 abstract description 17
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhou et al. | Do feature attribution methods correctly attribute features? | |
Kim et al. | Sanity simulations for saliency methods | |
Di Langosco et al. | Goal misgeneralization in deep reinforcement learning | |
Lones | How to avoid machine learning pitfalls: a guide for academic researchers | |
Wu et al. | IID-Net: Image inpainting detection network via neural architecture search and attention | |
Alwassel et al. | Diagnosing error in temporal action detectors | |
Rahimi et al. | Toward requirements specification for machine-learned components | |
Temple et al. | Using machine learning to infer constraints for product lines | |
Ganiyusufoglu et al. | Spatio-temporal features for generalized detection of deepfake videos | |
Bashir et al. | Testing object-oriented software: life cycle Solutions | |
Možina et al. | Argument based machine learning applied to law | |
Delamaro et al. | Using concepts of content‐based image retrieval to implement graphical testing oracles | |
Rosani et al. | Eventmask: A game-based framework for event-saliency identification in images | |
Santos et al. | An LP-based approach for goal recognition as planning | |
Kim et al. | Learn, generate, rank, explain: A case study of visual explanation by generative machine learning | |
Robberechts et al. | un-xPass: Measuring Soccer Player's Creativity | |
Zhao et al. | ODAM: Gradient-based instance-specific visual explanations for object detection | |
Taylor et al. | Using behaviour inference to optimise regression test sets | |
Yang et al. | Re-calibrating feature attributions for model interpretation | |
Vastel et al. | FP-tester: automated testing of browser fingerprint resilience | |
CN108229285A (en) | Object classification method, the training method of object classification device, device and electronic equipment | |
CN105630680B (en) | Random test program generation method | |
Parker et al. | Machine Learning Classification of Obfuscation using Image Visualization. | |
Wiesen et al. | The anatomy of hardware reverse engineering: An exploration of human factors during problem solving | |
de Santiago et al. | Testing environmental models supported by machine learning |