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Found 5 papers, 1 papers with code
Are We Using Autoencoders in a Wrong Way?
Autoencoders are certainly among the most studied and used Deep Learning models: the idea behind them is to train a model in order to reconstruct the same input data.
Improving plant disease classification by adaptive minimal ensembling
Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions.
Ranked #1 on
Image Classification
on PlantVillage
Revisiting ensembling for improving the performance of deep learning models
Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks.
Efficient Adaptive Ensembling for Image Classification
In recent times, with the exception of sporadic cases, the trend in Computer Vision is to achieve minor improvements compared to considerable increases in complexity.
Ranked #1 on
Image Classification
on Oxford-IIIT Pets
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning
Our contribution to the classification of osteosarcoma cells is made as follows: a DL approach is applied to discriminate human Mesenchymal Stromal Cells (MSCs) from osteosarcoma cells and to classify the different cell populations under investigation.
