In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used
as strong baseline networks for audio classification. Even though there is a significant�…

Contrastive learning based vision-language joint pre-training has emerged as a successful
representation learning strategy. In this paper, we present a prototype representation�…

Recent developments in large-scale machine learning suggest that by scaling up data,
model size and training time properly, one might observe that improvements in pre-training�…

The relationship between face and disease has been discussed from thousands years ago,
which leads to the occurrence of facial diagnosis. The objective here is to explore the�…

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it
has gradually become the leading approach in many fields. It is currently playing a vital role�…

Self-supervised learning methods have witnessed a recent surge of interest after proving
successful in multiple application fields. In this work, we leverage these techniques, and we�…

Learning rate decay (lrDecay) is a\emph {de facto} technique for training modern neural
networks. It starts with a large learning rate and then decays it multiple times. It is empirically�…

Diabetic Retinopathy (DR)-a complication developed due to heightened blood glucose
levels-is deemed one of the most sight-threatening diseases. Unfortunately, DR screening is�…

We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies
(NSA), for training an end-to-end model for anomaly detection and localization using only�…

Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific�…