Multi-task vision problems can often be decomposed into separate tasks and stages, e.g., separating feature extraction and model building, or training independent models for each task. Joint optimization has been shown to improve performance, but can be difficult to apply to deep convolution neural networks (DCNN), especially with unbalanced data. This paper introduces a novel mixed objective optimization network (MOON), with a loss function which mixes errors from multiple tasks and supports domain adaptation when label frequencies differ between training and operational testing. Experiments demonstrate that not only does MOON advance the state of the art in facial attribute recognition, but it also outperforms independently trained DCNNs using the same data. Loss1 160K training images w 40 labels 40 Nets = 5.5 Billon Parameters SoJmax Classifica+on Training SVM Training DCNN Features 138M Parameters CASIA Webface Separate end--to--end DCNNs Features + Classifiers 40 Independent SVMs for predic+on OpJmizaJon Network Fig. 1 : Three approaches to attribute learning (and other multitask problems). On the left is a conceptual model of the current and recent stateof-the-art approaches, with features trained for classification problems then adapted as inputs to independent SVMs for prediction. The middle approach attacks the problem with separately trained Deep Convolution Neural Networks (DCNNs). While we demonstrate that this advances the state of the art in attribute accuracy, the cost is prohibitive for practical use. This papers asks, "How can a single network outperform the separately trained DCNNs?" On the right, is our answer, the mixed objective optimization network (MOON) architecture with a domain adapting multi-task DCNN loss. The MOON approach allows one network to efficiently learn to simultaneously output predictions for all attributes, with reduced training and storage costs, while producing better accuracy than independently trained DCNNs.
Rudd et al. REF proposed a mixed objective optimization network based on joint optimization over all the attributes.
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
ArXiv
ArXiv
Computer Science