Deep Decision Network for Multi-class Image Classification

By Venkatesh N. Murthy, Vivek Kumar Singh, Terrence Chen, Raghavan Manmatha, Dorin Comaniciu. 2016

86 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

DeepSetNet: Predicting Sets with Deep Neural Networks (2016, background): “Consequently, a large body of literature focused on single-class prediction [22, 38, 49, 30].”
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade (2017, background): “Network cascades [2, 18, 26, 33, 24] have been studied to improve the performance in classification [26], detection [18], and pose estimation [33].”
SegNBDT: Visual Decision Rules for Segmentation (2020, background): “Interpretable Segmentation Models: Likwise, recent XAI work tackles directly interpretable classification models [32, 18, 23, 1, 4], but analogous work in segmentation is sparse.”
Anytime Recognition with Routing Convolutional Networks (2019, methodology): “[ 30] builds a network that splits the data into disjoint clusters of classes, which could be handled by the subsequent expert networks for multi-class clas-siﬁcation.”
Learning More Universal Representations for Transfer-Learning (2017, methodology): “The methods of the second approach [23], [24], [25], [26], [27], [28] tackle the problem of undesirably mixed features in joint training.”
NBDT: Neural-Backed Decision Trees (2020, methodology): “Our baselines are either taken directly from the original papers or improved using a modern backbone: Deep Neural Decision Forest (DNDF updated with ResNet18) [16], Explainable Observer-Classifier (XOC) [2], Deep Convolutional Decision Jungle (DCDJ) [3], Network of Experts (NofE) [1], Deep Decision ”
Bi-Directional Cascade Network for Perceptual Edge Detection (2019, methodology): “[37] treat easy and hard samples with different networks to improve classification accuracy.”
Learning Decision Trees Recurrently Through Communication (2021, methodology): “To improve their performance, combining decision trees with neural networks has been explored by building hierarchical classifiers [46, 4, 65, 64], by transferring models [25, 55, 17, 23], and through regularization [61].”
Do Convolutional Neural Networks Learn Class Hierarchy? (2017, methodology): “In machine-learning literature, confusion matrices are often generated using the default Jet color map in MATLAB [22, 32, 46].”
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning (2018, methodology): “DCNNs are also much faster than handcrafted methods at deployment phase.”