# Learning Across Tasks and Domains

arXiv: https://arxiv.org/abs/1904.04744 · HTML: https://mlsanity.com/paper/1904.04744/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 16 of 16)

- [Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation](https://mlsanity.com/paper/2104.13613/) (2021)
- [GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature](https://mlsanity.com/paper/2204.00179/) (2022)
- [Learning optical flow from still images](https://mlsanity.com/paper/2104.03965/) (2021)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation](https://mlsanity.com/paper/2108.12545/) (2021)
- [Semi-supervised Multi-task Learning for Semantics and Depth](https://mlsanity.com/paper/2110.07197/) (2021)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation](https://mlsanity.com/paper/2304.02991/) (2023)
- [Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2110.06685/) (2021)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Self-Supervised Learning in Multi-Task Graphs through Iterative Consensus Shift](https://mlsanity.com/paper/2103.14417/) (2021)
- [Sensor-Guided Optical Flow](https://mlsanity.com/paper/2109.15321/) (2021)
- [Learning Good Features to Transfer Across Tasks and Domains](https://mlsanity.com/paper/2301.11310/) (2023)
- [A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design](https://mlsanity.com/paper/2303.04315/) (2023)
- [Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation](https://mlsanity.com/paper/2107.04523/) (2021)
- [On the Viability of Monocular Depth Pre-training for Semantic Segmentation](https://mlsanity.com/paper/2203.13987/) (2022)

## Abstract (arXiv)

Recent works have proven that many relevant visual tasks are closely related one to another. Yet, this connection is seldom deployed in practice due to the lack of practical methodologies to transfer learned concepts across different training processes. In this work, we introduce a novel adaptation framework that can operate across both task and domains. Our framework learns to transfer knowledge across tasks in a fully supervised domain (e.g., synthetic data) and use this knowledge on a different domain where we have only partial supervision (e.g., real data). Our proposal is complementary to existing domain adaptation techniques and extends them to cross tasks scenarios providing additional performance gains. We prove the effectiveness of our framework across two challenging tasks (i.e., monocular depth estimation and semantic segmentation) and four different domains (Synthia, Carla, Kitti, and Cityscapes).