Self-paced Deep Regression Forests with Consideration on Ranking Fairness

This is official codes for paper Self-paced Deep Regression Forests with Consideration on Ranking Fairness. In this paper, we proposes a new self-paced paradigm for deep discriminative model, which distinguishes noisy and underrepresented examples according to the output likelihood and entropy associated with each example, and we tackle the fundamental ranking problem in SPL from a new perspective: Fairness.

Why should we consider the fairness of self-paced learning?

We find that SPL focuses on easy samples at early pace and the underrepresented ones are always ranked at the end of the whole sequence. This phenomenon demonstrates the SPL has a potential sorting fairness issue. However, SPUDRFs considers sample uncertainty when ranking samples, thus making underrepresented samples be selected at early pace.

Tasks and Performances

Age Estimation on MORPH II Dataset

The gradual learning process of SP-DRFs and SPUDRFs. Left: The typical worst cases at each iteration. Right: The MAEs of SP-DRFs and SPUDRFs at each pace descend gradually. Compared with SP-DRFs, the SPUDRFs show its superiority of taking predictive uncertainty into consideration.

Gaze Estimation on MPII Dataset

The similar phenomena can be observed on MPII dataset.

Head Pose Estimation on BIWI Dataset

For visualization, we plot the leaf node distribution of SP-DRFs and SPUDRFs in gradual learning process. The means of leaf nodes of SP-DRFs gather in a small range, incurring seriously biased solutions, while that of SPUDRFs distribute widely, leading to much better MAE performance.

Fairness Evaluation

We use FRIA, proposed in our paper, as fairness metric. FAIR is defined as following form.

The following table shows the FAIR of different methods on different datasets. SPUDRFs achieve the best performance on all datasets.

Dataset	MORPH	FGNET	BIWI	BU-3DFE	MPII
DRFs	0.46	0.42	0.46	0.740	0.67
SP-DRFs	0.44	0.37	0.43	0.72	0.67
SPUDRFs	0.48	0.42	0.70	0.76	0.69

How to train your SPUDRFs

Pre-trained models and Dataset

We use pre-trained models for our training. You can download VGGFace from here and VGG IMDB-WIKI from here. The datasets used in our experiment are in following table. We use MTCNN to detect and align face. For BIWI, we use depth images. For MPII, we use normalized left eye and right eye patch as input, and details about normalization can be found here.

Task	Dataset
Age Estimation	MOPRH and FG-NET
Head Estimation	BIWI and BU-3DFE
Gaze Estimation	MPII

Environment setup

All codes are based on Pytorch, before you run this repo, please make sure that you have a pytorch envirment. You can install them using following command.

pip install -r requirements.txt

Train SPUDRFs

Code descritption:

Here is the description of the main codes.

step.py:         train SPUDRFs from scratch  
train.py:        complete one pace training for a given train set
predict.py:      complete a test for a given test set
picksamples.py:  select samples for next pace   

Train your SPUDRFs from scratch:

You should download this repo, and prepare your datasets and pre-trained models, then just run following command to train your SPUDRFs from scratch.

• Clone this repo:

git clone https://github.com/learninginvision/SPUDRFs.git  
cd SPUDFRs  

• Set config.yml

lr: 0.00002
max_step: 80000
batchsize: 32

total_pace: 10
pace_percent: [0.5, 0.0556, 0.0556, 0.0556, 0.0556, 0.0556, 0.0556, 0.0556, 0.0556, 0.0552]
alpha: 2
threshold: -3.0
ent_pick_per: 0
capped: False

• Train from scratch

python step.py

Acknowledgments

This code is inspired by caffe-DRFs.