Search Results for author:
Found 21 papers, 1 papers with code
EdgeOL: Efficient in-situ Online Learning on Edge Devices
Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-in inference requests and ii) adapting to possible deployment scenario changes.
Achieve Fairness without Demographics for Dermatological Disease Diagnosis
To overcome this, we propose a method enabling fair predictions for sensitive attributes during the testing phase without using such information during training.
Additional Positive Enables Better Representation Learning for Medical Images
This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability.
Self-supervised On-device Federated Learning from Unlabeled Streams
The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge.
Enabling Weakly-Supervised Temporal Action Localization from On-Device Learning of the Video Stream
To enable W-TAL models to learn from a long, untrimmed streaming video, we propose an efficient video learning approach that can directly adapt to new environments.
Action Detection
Weakly-supervised Temporal Action Localization
+1
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels.
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization
The framework is divided into two stages: In the first in-FL stage, clients with different skin types are trained in a federated learning process to construct a global model for all skin types.
Distributed Contrastive Learning for Medical Image Segmentation
However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective.
Federated Contrastive Learning for Volumetric Medical Image Segmentation
However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis
By pruning the parameters based on this importance difference, we can reduce the accuracy difference between the privileged group and the unprivileged group to improve fairness without a large accuracy drop.
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices
Results show that FaHaNa can identify a series of neural networks with higher fairness and accuracy on a dermatology dataset.
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
The recently developed self-supervised learning approach, contrastive learning (CL), can leverage the unlabeled data to pre-train a model, after which the model is fine-tuned on limited labeled data for dermatological disease diagnosis.
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning
To tackle this problem, we propose a data generation framework with two methods to improve CL training by joint sample generation and contrastive learning.
Decentralized Unsupervised Learning of Visual Representations
To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations.
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching
Federated learning (FL) enables distributed clients to learn a shared model for prediction while keeping the training data local on each client.
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation
In this way, the main model learns to cluster hard positives by pulling the representations of similar yet distinct samples together, by which the representations of similar samples are well-clustered and better representations can be learned.
Positional Contrastive Learning for Volumetric Medical Image Segmentation
The success of deep learning heavily depends on the availability of large labeled training sets.
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy.
Enabling Efficient On-Device Self-supervised Contrastive Learning by Data Selection
In this paper, we propose a framework to automatically select the most representative data from unlabeled input stream on-the-fly, which only requires the use of a small data buffer for dynamic learning.
Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning
For example, when training ResNet-110 on CIFAR-10, we achieve 68% computation saving while preserving full accuracy and 75% computation saving with a marginal accuracy loss of 1. 3%.
Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices
This work aims to enable persistent, event-driven sensing and decision capabilities for energy-harvesting (EH)-powered devices by deploying lightweight DNNs onto EH-powered devices.
