Papers with Particularly demanding
Domain-Agnostic Neural Architecture for Class Incremental Continual Learning in Document Processing Platform (2023.acl-industry)
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| Challenge: | Recent methods with stochastic gradient learning struggle in streaming data setups and are restricted to specific domains. |
| Approach: | They propose a fully differentiable architecture that enables the training of high-performance classifiers when examples from each class are presented separately. |
| Outcome: | The proposed architecture achieves SOTA results without a memory buffer and clearly outperforms the reference methods. |