# Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples

arXiv: https://arxiv.org/abs/2004.01459 · HTML: https://mlsanity.com/paper/2004.01459/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression](https://mlsanity.com/paper/2206.02338/) (2022)
- [Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression](https://mlsanity.com/paper/2204.00309/) (2022)
- [NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research](https://mlsanity.com/paper/2211.11747/) (2022)
- [PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation](https://mlsanity.com/paper/2204.11548/) (2022)
- [MetaAge: Meta-Learning Personalized Age Estimators](https://mlsanity.com/paper/2207.05288/) (2022)
- [General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone](https://mlsanity.com/paper/2307.10129/) (2023)
- [Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests](https://mlsanity.com/paper/2402.14080/) (2024)
- [Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning](https://mlsanity.com/paper/2106.02824/) (2021)
- [Self-Paced Deep Regression Forests with Consideration of Ranking Fairness](https://mlsanity.com/paper/2112.06455/) (2021)
- [GAGA: Deciphering Age-path of Generalized Self-paced Regularizer](https://mlsanity.com/paper/2209.07063/) (2022)

## Abstract (arXiv)

Deep discriminative models (e.g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation. Most existing methods pursue robust and unbiased solutions either through learning discriminative features, or reweighting samples. We argue what is more desirable is learning gradually to discriminate like our human beings, and hence we resort to self-paced learning (SPL). Then, a natural question arises: can self-paced regime lead deep discriminative models to achieve more robust and less biased solutions? To this end, this paper proposes a new deep discriminative model--self-paced deep regression forests with consideration on underrepresented examples (SPUDRFs). It tackles the fundamental ranking and selecting problem in SPL from a new perspective: fairness. This paradigm is fundamental and could be easily combined with a variety of deep discriminative models (DDMs). Extensive experiments on two computer vision tasks, i.e., facial age estimation and head pose estimation, demonstrate the efficacy of SPUDRFs, where state-of-the-art performances are achieved.