论文标题
机器学习背后的统计基础及其对计算机视觉的影响
Statistical Foundation Behind Machine Learning and Its Impact on Computer Vision
论文作者
论文摘要
本文重新审查了统计学习中统一融合的原则，讨论了它是机器学习背后的基础，并试图更好地了解当前深度学习算法正在解决的基本问题。讨论以计算机视觉为示例领域，表明，利用越来越大规模的数据进行预训练的最新研究趋势在很大程度上是为了减少实际上可探讨的经验损失与最终所期望的但可靠的预期损失之间的差异。此外，本文提出了一些未来的研究方向，可以预测数据的持续增加，并认为通过结合结构和知识，需要对机器学习的鲁棒性，可解释性和推理能力进行更多的基础研究。
This paper revisits the principle of uniform convergence in statistical learning, discusses how it acts as the foundation behind machine learning, and attempts to gain a better understanding of the essential problem that current deep learning algorithms are solving. Using computer vision as an example domain in machine learning, the discussion shows that recent research trends in leveraging increasingly large-scale data to perform pre-training for representation learning are largely to reduce the discrepancy between a practically tractable empirical loss and its ultimately desired but intractable expected loss. Furthermore, this paper suggests a few future research directions, predicts the continued increase of data, and argues that more fundamental research is needed on robustness, interpretability, and reasoning capabilities of machine learning by incorporating structure and knowledge.
