# Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Prediction

arXiv: https://arxiv.org/abs/2108.04353 · HTML: https://mlsanity.com/paper/2108.04353/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 70 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification](https://mlsanity.com/paper/1611.05377/) (2016)
- [Deep Multi-task Representation Learning: A Tensor Factorisation Approach](https://mlsanity.com/paper/1605.06391/) (2016)
- [Trace Norm Regularised Deep Multi-Task Learning](https://mlsanity.com/paper/1606.04038/) (2016)
- [An All-In-One Convolutional Neural Network for Face Analysis](https://mlsanity.com/paper/1611.00851/) (2016)
- [Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach](https://mlsanity.com/paper/1706.00906/) (2017)
- [MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes](https://mlsanity.com/paper/1603.07027/) (2016)
- [PANDA: Pose Aligned Networks for Deep Attribute Modeling](https://mlsanity.com/paper/1311.5591/) (2013)
- [Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification](https://mlsanity.com/paper/2002.03683/) (2020)
- [GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning](https://mlsanity.com/paper/1804.06964/) (2018)
- [Improving Facial Attribute Prediction using Semantic Segmentation](https://mlsanity.com/paper/1704.08740/) (2017)
- [Multi-task Learning of Cascaded CNN for Facial Attribute Classification](https://mlsanity.com/paper/1805.01290/) (2018)
- [Attribute-Guided Coupled GAN for Cross-Resolution Face Recognition](https://mlsanity.com/paper/1908.01790/) (2019)
- [Slim-CNN: A Light-Weight CNN for Face Attribute Prediction](https://mlsanity.com/paper/1907.02157/) (2019)
- [Face Attribute Prediction Using Off-the-Shelf CNN Features](https://mlsanity.com/paper/1602.03935/) (2016)
- [Segment-based Methods for Facial Attribute Detection from Partial Faces](https://mlsanity.com/paper/1801.03546/) (2018)
- [A Deep Face Identification Network Enhanced by Facial Attributes Prediction](https://mlsanity.com/paper/1805.00324/) (2018)
- [Deep Sketch-Photo Face Recognition Assisted by Facial Attributes](https://mlsanity.com/paper/1808.00059/) (2018)
- [A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction](https://mlsanity.com/paper/1804.02810/) (2018)
- [Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval](https://mlsanity.com/paper/1902.04139/) (2019)
- [Residual Codean Autoencoder for Facial Attribute Analysis](https://mlsanity.com/paper/1803.07386/) (2018)
- [Multi-task Learning over Graph Structures](https://mlsanity.com/paper/1811.10211/) (2018)

## Built on by (most cited first, 3 of 3)

- [Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning](https://mlsanity.com/paper/2302.11102/) (2023)
- [LogicNet: A Logical Consistency Embedded Face Attribute Learning Network](https://mlsanity.com/paper/2311.11208/) (2023)
- [Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder](https://mlsanity.com/paper/2111.07846/) (2021)

## Abstract (arXiv)

The great success of Convolutional Neural Networks (CNN) for facial attribute prediction relies on a large amount of labeled images. Facial image datasets are usually annotated by some commonly used attributes (e.g., gender), while labels for the other attributes (e.g., big nose) are limited which causes their prediction challenging. To address this problem, we use a new Multi-Task Learning (MTL) paradigm in which a facial attribute predictor uses the knowledge of other related attributes to obtain a better generalization performance. Here, we leverage MLT paradigm in two problem settings. First, it is assumed that the structure of the tasks (e.g., grouping pattern of facial attributes) is known as a prior knowledge, and parameters of the tasks (i.e., predictors) within the same group are represented by a linear combination of a limited number of underlying basis tasks. Here, a sparsity constraint on the coefficients of this linear combination is also considered such that each task is represented in a more structured and simpler manner. Second, it is assumed that the structure of the tasks is unknown, and then structure and parameters of the tasks are learned jointly by using a Laplacian regularization framework. Our MTL methods are compared with competing methods for facial attribute prediction to show its effectiveness.