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Found 8 papers, 5 papers with code
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
Employing DFL methods to solve such general optimization problems leads to the formulation of Decentralized Nonconvex Composite Federated Learning (DNCFL), a topic that remains largely underexplored.
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
To overcome these limitations, we propose a novel composite federated learning algorithm called \textbf{FedCanon}, designed to solve the optimization problems comprising a possibly non-convex loss function and a weakly convex, potentially non-smooth regularization term.
DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection
A commonly-used strategy for supervised IOD is to encourage the current model (as a student) to mimic the behavior of the old model (as a teacher), but it generally fails in SSIOD because a dominant number of object instances from old and new classes are coexisting and unlabelled, with the teacher only recognizing a fraction of them.
Multi-Scale Occ: 4th Place Solution for CVPR 2023 3D Occupancy Prediction Challenge
In this report, we present the 4th place solution for CVPR 2023 3D occupancy prediction challenge.
Ranked #3 on
Prediction Of Occupancy Grid Maps
on Occ3D-nuScenes
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving
However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems.
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
To the best of our knowledge, WS-3D-Lane is the first try of 3D lane detection under weakly supervised setting.
DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks
We also test DualConv for image detection on YOLO-V3.
Improving the Speed and Quality of GAN by Adversarial Training
Generative adversarial networks (GAN) have shown remarkable results in image generation tasks.
