压力和适应：在深度学习中应用Anna Karenina原则进行图像分类
Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification
摘要：
使用深度神经网络的图像分类已经达到了高精度的最新水平。这一成功归功于良好的内部表示功能，绕过了非凸优化问题的困难。我们对这些内部表征知之甚少，更不用说量化它们了。最近的研究工作集中在替代理论和解释这些深层网络的普遍性。我们提出了在训练过程中对深度模型的替代扰动会引起导致过渡到不同家族的变化。其结果是深度学习的安娜·卡列尼娜原则AKP，其中较难概括的模型不幸福的家庭在其表示方面比更易概括的模型幸福的家庭变化更大，这与列夫·托尔斯泰的格言相似，即所有幸福的家庭看起来都一样，每个不幸的家庭都以自己的方式不幸福。安娜·卡列尼娜原则在广泛的系统中被发现：从暴露在恶劣天气下的濒危珊瑚表面到患有致命艾滋病的患者的肺部。在我们的论文中，我们通过在训练过程中热交换激活和损失函数来对我们的模型产生人工扰动。在本文中，我们建立了一个模型来分类癌细胞和非癌细胞。我们给出了理论证明，可推广的快乐模型的内部表示在渐近极限上是相似的。我们的实验验证了类似的可推广的模型表示。
英文摘要：
Image classification with deep neural networks has reached state-of-art with high accuracy. This success is attributed to good internal representation features that bypasses the difficulties of the non-convex optimization problems. We have little understanding of these internal representations, let alone quantifying them. Recent research efforts have focused on alternative theories and explanations of the generalizability of these deep networks. We propose the alternative perturbation of deep models during their training induces changes that lead to transitions to different families. The result is an Anna Karenina Principle AKP for deep learning, in which less generalizable models unhappy families vary more in their representation than more generalizable models happy families paralleling Leo Tolstoy dictum that all happy families look alike, each unhappy family is unhappy in its own way. Anna Karenina principle has been found in systems in a wide range: from the surface of endangered corals exposed to harsh weather to the lungs of patients suffering from fatal diseases of AIDs. In our paper, we have generated artificial perturbations to our model by hot-swapping the activation and loss functions during the training. In this paper, we build a model to classify cancer cells from non-cancer ones. We give theoretical proof that the internal representations of generalizable happy models are similar in the asymptotic limit. Our experiments verify similar representations of generalizable models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
