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Self Training with Ensemble of Teacher Models
We perform several experiments on the popular STL-10 database followed by an extensive analysis of our approach and study its effects on model accuracy and calibration.
Unravelling Small Sample Size Problems in the Deep Learning World
However, there are a lot of \textit{small sample size or $S^3$} problems for which it is not feasible to collect large training databases.
On Learning Density Aware Embeddings
The proposed method, termed as Density Aware Metric Learning, enforces the model to learn embeddings that are pulled towards the most dense region of the clusters for each class.
Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology.
