Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2022
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2205.10952 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915023306096640 |
|---|---|
| author | Lee, Jung Hoon Vijayan, Sujith |
| author_facet | Lee, Jung Hoon Vijayan, Sujith |
| contents | Deep neural networks (DNNs), the agents of deep learning (DL), require a massive number of parallel/sequential operations, which makes it difficult to comprehend them and impedes proper diagnosis. Without better knowledge of DNNs' internal process, deploying DNNs in high-stakes domains may lead to catastrophic failures. Therefore, to build more reliable DNNs/DL, it is imperative that we gain insights into their underlying decision-making process. Here, we use the self-organizing map (SOM) to analyze DL models' internal codes associated with DNNs' decision-making. Our analyses suggest that shallow layers close to the input layer map onto homogeneous codes and that deep layers close to the output layer transform these homogeneous codes in shallow layers to diverse codes. We also found evidence indicating that homogeneous codes may underlie DNNs' vulnerabilities to adversarial perturbations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_10952 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Generalization ability and Vulnerabilities to adversarial perturbations: Two sides of the same coin Lee, Jung Hoon Vijayan, Sujith Machine Learning Deep neural networks (DNNs), the agents of deep learning (DL), require a massive number of parallel/sequential operations, which makes it difficult to comprehend them and impedes proper diagnosis. Without better knowledge of DNNs' internal process, deploying DNNs in high-stakes domains may lead to catastrophic failures. Therefore, to build more reliable DNNs/DL, it is imperative that we gain insights into their underlying decision-making process. Here, we use the self-organizing map (SOM) to analyze DL models' internal codes associated with DNNs' decision-making. Our analyses suggest that shallow layers close to the input layer map onto homogeneous codes and that deep layers close to the output layer transform these homogeneous codes in shallow layers to diverse codes. We also found evidence indicating that homogeneous codes may underlie DNNs' vulnerabilities to adversarial perturbations. |
| title | Generalization ability and Vulnerabilities to adversarial perturbations: Two sides of the same coin |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2205.10952 |