Similarity-Based Clustering for Enhancing Image Classification
AbstractConvolutional networks are at the center of best in class computer
vision applications for a wide assortment of undertakings. Since 2014,
profound amount of work began to make better convolutional
architectures, yielding generous additions in different benchmarks.
Albeit expanded model size and computational cost will, in general, mean
prompt quality increases for most undertakings but, the architectures
now need to have some additional information to increase the
performance. We show empirical evidence that with the amalgamation of
content-based image similarity and deep learning models, we can provide
the flow of information which can be used in making clustered learning
possible. We show how parallel training of sub-dataset clusters not only
reduces the cost of computation but also increases the benchmark
accuracies by 5-11 percent.