Intra-Class Mixup for Out-of-Distribution Detection
Deep neural networks (DNNs) have found widespread adoption in solving image recognition and natural language processing tasks. However, they make confident mispredictions when presented with data that does not belong to the training distribution, i.e. out-of-distribution (OoD) samples. Research has...
I tiakina i:
| Ngā kaituhi matua: | , , , |
|---|---|
| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
IEEE
2023-01-01
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| Rangatū: | IEEE Access |
| Ngā marau: | |
| Urunga tuihono: | https://ieeexplore.ieee.org/document/10064300/ |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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