# Learning Fine-grained Features via a CNN Tree for Large-scale Classification Source: [Learning Fine-grained Features via a CNN Tree for Large-scale Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:46:17.527Z Authors: Zhenhua Wang, Xingxing Wang, Gang Wang Published: 2015-11-14T09:19:44.000Z ## Abstract We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes. Such features are expected to be more discriminative, compared to features learned for all the classes. We develop a new algorithm to effectively learn the tree structure from a large number of classes. Experiments on large-scale image classification tasks demonstrate that our method could boost the performance of a given basic CNN model. Our method is quite general, hence it can potentially be used in combination with many other deep learning models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()