# You Only Learn One Representation: Unified Network for Multiple Tasks

arXiv: https://arxiv.org/abs/2105.04206 · HTML: https://mlsanity.com/paper/2105.04206/

## Key facts

- Cited by 86 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Deep Equilibrium Models](https://mlsanity.com/paper/1909.01377/) (2019)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Multiscale Deep Equilibrium Models](https://mlsanity.com/paper/2006.08656/) (2020)
- [Disentangled Non-Local Neural Networks](https://mlsanity.com/paper/2006.06668/) (2020)
- [Probabilistic two-stage detection](https://mlsanity.com/paper/2103.07461/) (2021)
- [Implicit Feature Pyramid Network for Object Detection](https://mlsanity.com/paper/2012.13563/) (2020)

## Built on by (most cited first, 30 of 86)

- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Guided Image Synthesis via Initial Image Editing in Diffusion Model](https://mlsanity.com/paper/2305.03382/) (2023)
- [YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception](https://mlsanity.com/paper/2506.17733/) (2025)
- [Designing Network Design Strategies Through Gradient Path Analysis](https://mlsanity.com/paper/2211.04800/) (2022)
- [YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception](https://mlsanity.com/paper/2208.11434/) (2022)
- [The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization](https://mlsanity.com/paper/2312.08872/) (2023)
- [ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking](https://mlsanity.com/paper/2202.01811/) (2022)
- [FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection](https://mlsanity.com/paper/2305.17449/) (2023)
- [Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation](https://mlsanity.com/paper/2207.10825/) (2022)
- [The 8th AI City Challenge](https://mlsanity.com/paper/2404.09432/) (2024)
- [Non-deep Networks](https://mlsanity.com/paper/2110.07641/) (2021)
- [Object Detection in Autonomous Vehicles: Status and Open Challenges](https://mlsanity.com/paper/2201.07706/) (2022)
- [A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance](https://mlsanity.com/paper/2207.12926/) (2022)
- [Computation-efficient Deep Learning for Computer Vision: A Survey](https://mlsanity.com/paper/2308.13998/) (2023)
- [Training-Free Location-Aware Text-to-Image Synthesis](https://mlsanity.com/paper/2304.13427/) (2023)
- [Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm](https://mlsanity.com/paper/2304.05071/) (2023)
- [Toloka Visual Question Answering Benchmark](https://mlsanity.com/paper/2309.16511/) (2023)
- [YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images](https://mlsanity.com/paper/2403.11249/) (2024)
- [Calibrating a Deep Neural Network with Its Predecessors](https://mlsanity.com/paper/2302.06245/) (2023)
- [Waste detection in Pomerania: non-profit project for detecting waste in environment](https://mlsanity.com/paper/2105.06808/) (2021)
- [Deep Learning for Logo Detection: A Survey](https://mlsanity.com/paper/2210.04399/) (2022)
- [Curriculum-based Asymmetric Multi-task Reinforcement Learning](https://mlsanity.com/paper/2211.03352/) (2022)
- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)
- [We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature](https://mlsanity.com/paper/2106.05261/) (2021)
- [Online V2X Scheduling for Raw-Level Cooperative Perception](https://mlsanity.com/paper/2202.06085/) (2022)
- [S4OD: Semi-Supervised learning for Single-Stage Object Detection](https://mlsanity.com/paper/2204.04492/) (2022)
- [EdgeYOLO: An Edge-Real-Time Object Detector](https://mlsanity.com/paper/2302.07483/) (2023)

## Abstract (arXiv)

People ``understand'' the world via vision, hearing, tactile, and also the past experience. Human experience can be learned through normal learning (we call it explicit knowledge), or subconsciously (we call it implicit knowledge). These experiences learned through normal learning or subconsciously will be encoded and stored in the brain. Using these abundant experience as a huge database, human beings can effectively process data, even they were unseen beforehand. In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning. The unified network can generate a unified representation to simultaneously serve various tasks. We can perform kernel space alignment, prediction refinement, and multi-task learning in a convolutional neural network. The results demonstrate that when implicit knowledge is introduced into the neural network, it benefits the performance of all tasks. We further analyze the implicit representation learnt from the proposed unified network, and it shows great capability on catching the physical meaning of different tasks. The source code of this work is at : https://github.com/WongKinYiu/yolor.