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Article overview
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Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach | Cuong Nguyen
; Thanh-Toan Do
; Gustavo Carneiro
; | Date: |
4 Jan 2023 | Abstract: | Learning from noisy labels plays an important role in the deep learning era.
Despite numerous studies with promising results, identifying clean labels from
a noisily-annotated dataset is still challenging since the conventional noisy
label learning problem with single noisy label per instance is not
identifiable, i.e., it does not theoretically have a unique solution unless one
has access to clean labels or introduces additional assumptions. This paper
aims to formally investigate such identifiability issue by formulating the
noisy label learning problem as a multinomial mixture model, enabling the
formulation of the identifiability constraint. In particular, we prove that the
noisy label learning problem is identifiable if there are at least $2C - 1$
noisy labels per instance provided, with $C$ being the number of classes. In
light of such requirement, we propose a method that automatically generates
additional noisy labels per training sample by estimating the noisy label
distribution based on nearest neighbours. Such additional noisy labels allow us
to apply the Expectation - Maximisation algorithm to estimate the posterior of
clean labels. We empirically demonstrate that the proposed method is not only
capable of estimating clean labels without any heuristics in several
challenging label noise benchmarks, including synthetic, web-controlled and
real-world label noises, but also of performing competitively with many
state-of-the-art methods. | Source: | arXiv, 2301.01405 | Services: | Forum | Review | PDF | Favorites |
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