multi-layer representation learning
Multi-layer Representation Learning for Robust OOD Image Classification
Ballas, Aristotelis, Diou, Christos
Convolutional Neural Networks have become the norm in image classification. Nevertheless, their difficulty to maintain high accuracy across datasets has become apparent in the past few years. In order to utilize such models in real-world scenarios and applications, they must be able to provide trustworthy predictions on unseen data. In this paper, we argue that extracting features from a CNN's intermediate layers can assist in the model's final prediction. Specifically, we adapt the Hypercolumns method to a ResNet-18 and find a significant increase in the model's accuracy, when evaluating on the NICO dataset.
2207.13678
Country:
- Europe > Greece > Ionian Islands > Corfu (0.06)
- Europe > Greece > Attica > Athens (0.05)
- North America > United States > New York > New York County > New York City (0.04)
- North America > United States > Massachusetts > Suffolk County > Boston (0.04)
Technology: