Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images
CNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small impact on the loss function of the algorithm, wh...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/10/3/72 |
| _version_ | 1797579605998043136 |
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| author | Diego Renza Dora Ballesteros |
| author_facet | Diego Renza Dora Ballesteros |
| author_sort | Diego Renza |
| collection | DOAJ |
| description | CNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small impact on the loss function of the algorithm, which is known as pruning. Typically, pruning methods are compared in terms of performance (e.g., accuracy), model size and inference speed. However, it is unusual to evaluate whether a pruned model preserves regions of importance in an image when performing inference. Consequently, we propose a metric to assess the impact of a pruning method based on images obtained by model interpretation (specifically, class activation maps). These images are spatially and spectrally compared and integrated by the harmonic mean for all samples in the test dataset. The results show that although the accuracy in a pruned model may remain relatively constant, the areas of attention for decision making are not necessarily preserved. Furthermore, the performance of pruning methods can be easily compared as a function of the proposed metric. |
| first_indexed | 2024-03-10T22:38:34Z |
| format | Article |
| id | doaj.art-c3879c382f794bc8b6c88191403708b2 |
| institution | Directory Open Access Journal |
| issn | 2227-9709 |
| language | English |
| last_indexed | 2024-03-10T22:38:34Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj.art-c3879c382f794bc8b6c88191403708b22023-11-19T11:13:39ZengMDPI AGInformatics2227-97092023-09-011037210.3390/informatics10030072Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM ImagesDiego Renza0Dora Ballesteros1Facultad de Ingeniería, Universidad Militar Nueva Granada, Bogotá 110111, ColombiaFacultad de Ingeniería, Universidad Militar Nueva Granada, Bogotá 110111, ColombiaCNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small impact on the loss function of the algorithm, which is known as pruning. Typically, pruning methods are compared in terms of performance (e.g., accuracy), model size and inference speed. However, it is unusual to evaluate whether a pruned model preserves regions of importance in an image when performing inference. Consequently, we propose a metric to assess the impact of a pruning method based on images obtained by model interpretation (specifically, class activation maps). These images are spatially and spectrally compared and integrated by the harmonic mean for all samples in the test dataset. The results show that although the accuracy in a pruned model may remain relatively constant, the areas of attention for decision making are not necessarily preserved. Furthermore, the performance of pruning methods can be easily compared as a function of the proposed metric.https://www.mdpi.com/2227-9709/10/3/72class activation map (CAM)deep learningmodel compressionpruning evaluationspectral angle mapper (SAM)structural similarity index (SSIM) |
| spellingShingle | Diego Renza Dora Ballesteros Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images Informatics class activation map (CAM) deep learning model compression pruning evaluation spectral angle mapper (SAM) structural similarity index (SSIM) |
| title | Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images |
| title_full | Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images |
| title_fullStr | Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images |
| title_full_unstemmed | Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images |
| title_short | Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images |
| title_sort | sp2ps pruning score by spectral and spatial evaluation of cam images |
| topic | class activation map (CAM) deep learning model compression pruning evaluation spectral angle mapper (SAM) structural similarity index (SSIM) |
| url | https://www.mdpi.com/2227-9709/10/3/72 |
| work_keys_str_mv | AT diegorenza sp2pspruningscorebyspectralandspatialevaluationofcamimages AT doraballesteros sp2pspruningscorebyspectralandspatialevaluationofcamimages |