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Found 8 papers, 6 papers with code
PraFFL: A Preference-Aware Scheme in Fair Federated Learning
Fairness in federated learning has emerged as a critical concern, aiming to develop an unbiased model for any special group (e. g., male or female) of sensitive features.
Data-Driven Preference Sampling for Pareto Front Learning
In this manner, the proposed method can sample preference vectors from the location of the Pareto front with a high probability.
Evolutionary Preference Sampling for Pareto Set Learning
Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network.
Collaborative Pareto Set Learning in Multiple Multi-Objective Optimization Problems
In this paper, we propose a Collaborative Pareto Set Learning (CoPSL) framework, which simultaneously learns the Pareto sets of multiple MOPs in a collaborative manner.
Localization Distillation for Object Detection
Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and the absence of localization distillation is a critical reason for why logit mimicking underperforms for years.
Localization Distillation for Dense Object Detection
Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e. g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric.
