Invariant information clustering for unsupervised image classification and segmentation
We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classif...
| Main Authors: | , , |
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| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
IEEE
2020
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