We specifically aim to attack the widely used Faster R-CNN by changing the predicted label for a particular object in an image: where prior work has targeted one specific object (a stop sign), we generalise to arbitrary objects, with the key challenge being the need to change the labels of all bounding boxes for all instances of that object type.
In this paper, we study the behaviour of NMT systems when multiple changes are made to the source sentence.
This paper presents an approach for automatic detection of Munro's Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a machine assisted diagnosis of Psoriasis.
In this paper, a deep neural network based ensemble method is experimented for automatic identification of skin disease from dermoscopic images.
Several pattern recognition principles and state of art (SoA) ML techniques are followed to design the overall approach for the proposed automation.
It is found that except Bengali, the proposed method outperforms Lemming and Morfette on the other languages.
In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline.
Real forensic samples are involved in the experiment that shows a high precision machine can be developed for authentication of paper money.