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Found 13 papers, 0 papers with code
Explore the Power of Synthetic Data on Few-shot Object Detection
To construct a representative synthetic training dataset, we maximize the diversity of the selected images via a sample-based and cluster-based method.
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Specifically, we first discover the base images which contain the FP of novel categories and select a certain amount of samples from them for the base and novel categories balance.
Explore the Power of Dropout on Few-shot Learning
The generalization power of the pre-trained model is the key for few-shot deep learning.
A Unified Framework with Meta-dropout for Few-shot Learning
Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations.
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING
Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations.
Constructive neural network learning
In this paper, we aim at developing scalable neural network-type learning systems.
Re-scale boosting for regression and classification
Boosting is a learning scheme that combines weak prediction rules to produce a strong composite estimator, with the underlying intuition that one can obtain accurate prediction rules by combining "rough" ones.
Model selection of polynomial kernel regression
On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary.
Nonparametric regression using needlet kernels for spherical data
Due to the localization property in the frequency domain, we prove that the regularization parameter of the kernel ridge regression associated with the needlet kernel can decrease arbitrarily fast.
Greedy metrics in orthogonal greedy learning
Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a new atom from a dictionary via the steepest gradient descent and build the estimator via orthogonal projecting the target function to the space spanned by the selected atoms in each greedy step.
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)
On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning.
Learning rates of $l^q$ coefficient regularization learning with Gaussian kernel
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and $l^q$ regularization schemes with $0<q<\infty$ are central in use.
Does generalization performance of $l^q$ regularization learning depend on $q$? A negative example
To facilitate the use of $l^{q}$-regularization, we intend to seek for a modeling strategy where an elaborative selection on $q$ is avoidable.
