Search Results for author:
Found 6 papers, 3 papers with code
Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram
However, adapting to the application of screening disease is challenging in that labeled ECG data are limited.
Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection
While many real-world data streams imply that they change frequently in a nonstationary way, most of deep learning methods optimize neural networks on training data, and this leads to severe performance degradation when dataset shift happens.
Data Proxy Generation for Fast and Efficient Neural Architecture Search
We propose a systematic approach to measure the importance of each example from this relative accuracy ranking point of view, and make a reliable data proxy based on the statistics of training and testing examples.
JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels
The second is that we can use any kinds of weak labels or image features that have correlations with the original image data to enhance unconditional image generation.
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
In object detection, reducing computational cost is as important as improving accuracy for most practical usages.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recent technical innovations.
