论文标题
脱皮的学习，用于视觉模式的可解释的层次结构表示
Decontextualized learning for interpretable hierarchical representations of visual patterns
论文作者
论文摘要
除了用于分类和对象检测任务的判别模型外，使用自然成像数据的深卷卷神经网络在基础研究中的应用也有限。特别是在需要一组可解释的下游分析特征的情况下，许多科学研究的关键要求。我们提出了一种专门针对以下算法的算法和培训范式：脱皮化层次表示学习（DHRL）。通过将生成模型链接过程与梯子网络体系结构和推理潜在空间正则化结合，DHRL解决了小型数据集的局限性，并鼓励了一组分离的分层组织。除了提供使用变异推理分析复杂层次模式的可拖动路径外，这种方法是生成性的，可以直接与经验和理论方法结合使用。为了强调DHRL的可扩展性和实用性，我们在进化生物学的问题中证明了这种方法。
Apart from discriminative models for classification and object detection tasks, the application of deep convolutional neural networks to basic research utilizing natural imaging data has been somewhat limited; particularly in cases where a set of interpretable features for downstream analysis is needed, a key requirement for many scientific investigations. We present an algorithm and training paradigm designed specifically to address this: decontextualized hierarchical representation learning (DHRL). By combining a generative model chaining procedure with a ladder network architecture and latent space regularization for inference, DHRL address the limitations of small datasets and encourages a disentangled set of hierarchically organized features. In addition to providing a tractable path for analyzing complex hierarchal patterns using variation inference, this approach is generative and can be directly combined with empirical and theoretical approaches. To highlight the extensibility and usefulness of DHRL, we demonstrate this method in application to a question from evolutionary biology.
