Search Results for author:
Found 5 papers, 3 papers with code
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1.
Ranked #7 on
Real-Time Object Detection
on MS COCO
Designing Network Design Strategies Through Gradient Path Analysis
This paper proposes a new network design strategy, i. e., to design the network architecture based on gradient path analysis.
You Only Learn One Representation: Unified Network for Multiple Tasks
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.
Ranked #4 on
Object Detection
on COCO minival
(APS metric)
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization
Specifically, we are required to provide a solution that is able to (1) handle the traffic signal control when certain surveillance cameras that retrieve information for reinforcement learning are down, (2) learn from batch data without a traffic simulator, and (3) make control decisions without shared information across intersections.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
Ranked #673 on
Image Classification
on ImageNet
