6 papers
From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets
Sarina Penquitt, Jonathan Klees, Rinor Cakaj +3
Object detection has advanced rapidly in recent years, driven by increasingly large and diverse datasets. However, label errors often compromise the quality of these datasets and a…
Squeeze-and-Remember Block
Rinor Cakaj, Jens Mehnert, Bin Yang
Convolutional Neural Networks (CNNs) are important for many machine learning tasks. They are built with different types of layers: convolutional layers that detect features, dropou…
Spectral Wavelet Dropout: Regularization in the Wavelet Domain
Rinor Cakaj, Jens Mehnert, Bin Yang
Regularization techniques help prevent overfitting and therefore improve the ability of convolutional neural networks (CNNs) to generalize. One reason for overfitting is the comple…
CNN Mixture-of-Depths
Rinor Cakaj, Jens Mehnert, Bin Yang
We introduce Mixture-of-Depths (MoD) for Convolutional Neural Networks (CNNs), a novel approach that enhances the computational efficiency of CNNs by selectively processing channel…
Spectral Batch Normalization: Normalization in the Frequency Domain
Rinor Cakaj, Jens Mehnert, Bin Yang
Regularization is a set of techniques that are used to improve the generalization ability of deep neural networks. In this paper, we introduce spectral batch normalization (SBN), a…
Weight Compander: A Simple Weight Reparameterization for Regularization
Rinor Cakaj, Jens Mehnert, Bin Yang
Regularization is a set of techniques that are used to improve the generalization ability of deep neural networks. In this paper, we introduce weight compander (WC), a novel effect…