downsampling

Anti-Alias Downsampling

Introduction to Anti-Alias Downsampling Anti-Alias Downsampling (AA) is a technique used to improve the performance of deep learning networks. By reducing aliasing artifacts, it enhances the shift-equivariance of deep networks. AA works by implementing a low-pass filter between two operations of max-pooling. The first operation is to densely evaluate the max operator, and the second involves subsampling the output. AA is used to apply anti-aliasing to any existing strided layer, including strid

Synthetic Minority Over-sampling Technique.

What is SMOTE? SMOTE (Synthetic Minority Oversampling Technique) is a widely used approach to synthesizing new examples in machine learning. It was introduced by Nitesh Chawla and his research team in their 2002 paper titled “SMOTE: Synthetic Minority Over-sampling Technique.” How does SMOTE work? SMOTE works by generating synthetic examples in the feature space of a dataset. It creates new examples by selecting the minority class samples that are close to each other and creating synthetic d

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