Masashi Sugiyama, Gang Niu, Yivan Zhang
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| tv_regularization | Ran | this paper's copy was not recorded; identical code first harvested from YivanZhang/lio pointer only · get_code("9b2b174828d10d8b") |
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Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption. To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy classposterior, which could be unreliable due to the overconfidence of neural networks. In this work, we propose a theoretically grounded method that can estimate the noise transition matrix and learn a classifier simultaneously, without relying on the error-prone noisy class-posterior estimation. Concretely, inspired by the characteristics of the stochastic label corruption process, we propose total variation regularization, which encourages the predicted probabilities to be more distinguishable from each other. Under mild assumptions, the proposed method yields a consistent estimator of the transition matrix. We show the effectiveness of the proposed method through experiments on benchmark and real-world datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2102.02414")
get_code_for_paper("2102.02414")
have("2102.02414")
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