On explainability of deep neural networks
Deep Learning has attained state-of-the-art performance in the recent years, but it is still hard to determine the reasoning behind each prediction. This project will cover the latest advances on interpretability and propose a new method for pixel attribution on image classifiers.
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| Tipo de documento: | dissertação |
| Data de publicação: | 2018 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/121638 |
| Acesso em linha: | https://hdl.handle.net/2117/121638 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Neural networks (Computer science) Machine learning interpretabilitat explicabilitat visualització de característiques atribució DL ML CNN interpretability explainability feature visualization attribution xarxes neuronals xarxes neuronals convolucionals machine learning neural networks convolutional neural networks deep learning Xarxes neuronals (Informàtica) Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica |
| Resumo: | Deep Learning has attained state-of-the-art performance in the recent years, but it is still hard to determine the reasoning behind each prediction. This project will cover the latest advances on interpretability and propose a new method for pixel attribution on image classifiers. |
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