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[2107.11170] Bias Loss for Mobile Neural Networks
Source: arxiv.org
Article summary:
1. This paper proposes a novel Bias Loss to address the problem of random predictions in compact convolutional neural networks (CNNs).
2. The Bias Loss focuses the training on data points with a limited number of unique descriptive features, preventing samples with poor learning features from misleading the optimization process.
3. A family of SkipNet models are presented to boost the number of unique descriptors in the last layers, achieving 1% higher classification accuracy than MobileNetV3 Large.
Article analysis:
The article is generally reliable and trustworthy, as it provides evidence for its claims and presents both sides of an argument fairly. The authors provide a detailed explanation of their proposed Bias Loss and how it can be used to improve performance in compact CNNs. They also present a family of SkipNet models that can achieve higher classification accuracy than MobileNetV3 Large.
The article does not appear to have any major biases or one-sided reporting, as it presents both sides of an argument fairly and provides evidence for its claims. It does not contain any unsupported claims or missing points of consideration, as all relevant information is provided and discussed thoroughly. Additionally, there is no promotional content or partiality present in the article, as it is focused solely on providing scientific evidence for its claims.
The article does note possible risks associated with using the proposed Bias Loss, such as overfitting or underfitting due to incorrect hyperparameter settings. However, it does not explore any counterarguments or missing evidence for its claims made, which could have been beneficial in further strengthening its arguments.