arXiv:2111.11616
공식 코드 · Papers with Code
Using mixup as regularization and tuning hyper-parameters for ResNets
원문 초록
While novel computer vision architectures are gaining traction, the impact of model architectures is often related to changes or exploring in training methods. Identity mapping-based architectures ResNets and DenseNets have promised path-breaking results in the image classification task and are go-to methods for even now if the data given is fairly limited. Considering the ease of training with limited resources this work revisits the ResNets and improves the ResNet50 \cite{resnets} by using mixup data-augmentation as regularization and tuning the hyper-parameters.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation
Robust Training with Data Augmentation for Medical Imaging Classification
Compositional Attribute Imbalance in Vision Datasets
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery
DocXClassifier: High Performance Explainable Deep Network for Document Image Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.