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03/23/2023
Explore the Power of Synthetic Data on Few-shot Object Detection
Few-shot object detection (FSOD) aims to expand an object detector for n...
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02/28/2023
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Few-shot object detection (FSOD) aims to expand an object detector for n...
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01/26/2023
Explore the Power of Dropout on Few-shot Learning
The generalization power of the pre-trained model is the key for few-sho...
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10/12/2022
A Unified Framework with Meta-dropout for Few-shot Learning
Conventional training of deep neural networks usually requires a substan...
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03/22/2018
Learning through deterministic assignment of hidden parameters
Supervised learning frequently boils down to determining hidden and brig...
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03/01/2018
Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees
Training deep neural networks (DNNs) efficiently is a challenge due to t...
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05/06/2015
Re-scale boosting for regression and classification
Boosting is a learning scheme that combines weak prediction rules to pro...
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02/14/2015
Nonparametric regression using needlet kernels for spherical data
Needlets have been recognized as state-of-the-art tools to tackle spheri...
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12/19/2013
Learning rates of l^q coefficient regularization learning with Gaussian kernel
Regularization is a well recognized powerful strategy to improve the per...
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07/25/2013
