You Only Learn One Representation: Unified Network for Multiple Tasks (2105.04206v1)

Abstract: 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.

• The paper proposes a unified network that leverages both implicit and explicit knowledge to enhance multi-task learning.
• It refines kernel space alignment and prediction outputs, achieving an 88% boost in inference speed with negligible added parameters.
• Experimental results on MSCOCO demonstrate improved AP and performance across object detection, multi-label classification, and feature embedding tasks.

Overview of "You Only Learn One Representation: Unified Network for Multiple Tasks"

The paper presents a novel approach to multi-task learning through a unified network architecture that integrates implicit and explicit knowledge, enabling a general representation for various tasks. The research addresses the limitation of conventional neural networks, which typically focus on a single objective, by incorporating the concept of implicit knowledge—knowledge that is not directly related to observations and is often unutilized in traditional CNN architectures.

Key Methodologies and Contributions

The proposed unified network architecture leverages both implicit and explicit knowledge to improve task performance significantly. The process involves:

1. Implicit and Explicit Knowledge Integration: Utilizing a network structure where implicit knowledge supports explicit feature extraction, allowing the network to adapt to multiple tasks.
2. Kernel Space Alignment and Prediction Refinement: Introducing kernel space alignment and prediction refinement to enhance the neural network's capability in multi-task settings.
3. Modeling Implicit Knowledge: The paper explores various methods for modeling implicit knowledge, including vectors, neural networks, and matrix factorization, each offering distinct benefits.
4. Low Additional Cost: The proposed method incurs minimal additional computational costs (less than one ten-thousandth increase in parameters and calculations), making it efficient.
5. State-of-the-Art Object Detection: When combined with methods like Scaled-YOLOv4, the unified architecture achieves comparable results while significantly boosting inference speed by 88%.

Experimental Evaluation

The experiments, conducted using the MSCOCO dataset, demonstrated the model's effectiveness in tasks such as object detection, multi-label image classification, and feature embedding. Key findings include:

• Feature Alignment: Achieved a 0.5% increase in AP across different object sizes by applying implicit representations for feature space alignment.
• Prediction Refinement: Improved almost all detection metrics marginally by incorporating implicit representations in YOLO output layers.
• Multi-task Learning: Showed significant performance improvements when implicit representations were introduced in the joint detection-classification and joint detection-embedding tasks.

Theoretical and Practical Implications

The research extends both theoretical understanding and practical applications of multi-task learning. The incorporation of implicit knowledge offers a new dimension to neural network design, promising enhancements in performance without significant computational overhead. It opens avenues for more sophisticated models capable of handling complex tasks simultaneously.

Future Directions

The paper suggests potential future developments, particularly in expanding the unified network framework to handle multi-modal inputs, as depicted in their envisioned future architecture. This progress could lead to more versatile and efficient AI systems.

The work contributes substantially to the ongoing efforts in neural network optimization and multi-task learning, laying foundational principles for future breakthroughs in AI flexibility and capability.