Curriculum based dropout discriminator for domain adaptation

Domain adaptation is essential to enable wide usage of deep learning based networks trained using large labeled datasets. Adversarial learning based techniques have shown their utility towards solving this problem using a discriminator that ensures source and target distributions are close. However, here we suggest that rather than using a point estimate, it would be useful if a distribution based discriminator could be used to bridge this gap. This could be achieved using multiple classifiers or using traditional ensemble methods. In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator could suffice to obtain the distribution based discriminator. Specifically, we propose a curriculum based dropout discriminator that gradually increases the variance of the sample based distribution and the corresponding reverse gradients are used to align the source and target feature representations. The detailed results and thorough ablation analysis show that our model outperforms state-of-the-art results.Comment: BMVC 2019 Accepted, Project Page: https://delta-lab-iitk.github.io/CD3A

Deep bayesian network for visual question generation

Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. We observe that with the addition of more cues and by minimizing uncertainty among the cues, the Bayesian framework becomes more confident. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here we provide project link for Deep Bayesian VQG \url{https://delta-lab-iitk.github.io/BVQG/}Comment: WACV-2020 (Accepted

Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints

The effectiveness of fingerprint-based authentication systems on good quality fingerprints is established long back. However, the performance of standard fingerprint matching systems on noisy and poor quality fingerprints is far from satisfactory. Towards this, we propose a data uncertainty-based framework which enables the state-of-the-art fingerprint preprocessing models to quantify noise present in the input image and identify fingerprint regions with background noise and poor ridge clarity. Quantification of noise helps the model two folds: firstly, it makes the objective function adaptive to the noise in a particular input fingerprint and consequently, helps to achieve robust performance on noisy and distorted fingerprint regions. Secondly, it provides a noise variance map which indicates noisy pixels in the input fingerprint image. The predicted noise variance map enables the end-users to understand erroneous predictions due to noise present in the input image. Extensive experimental evaluation on 13 publicly available fingerprint databases, across different architectural choices and two fingerprint processing tasks demonstrate effectiveness of the proposed framework.Comment: IJCNN 2021 (Accepted

Development of an occupational airborne chemical exposure matrix

Background Population-based studies of the occupational contribution to chronic obstructive pulmonary disease generally rely on self-reported exposures to vapours, gases, dusts and fumes (VGDF), which are susceptible to misclassification. Aims To develop an airborne chemical job exposure matrix (ACE JEM) for use with the UK Standard Occupational Classification (SOC 2000) system. Methods We developed the ACE JEM in stages: (i) agreement of definitions, (ii) a binary assignation of exposed/not exposed to VGDF, fibres or mists (VGDFFiM), for each of the individual 353 SOC codes and (iii) assignation of levels of exposure (L; low, medium and high) and (iv) the proportion of workers (P) likely to be exposed in each code. We then expanded the estimated exposures to include biological dusts, mineral dusts, metals, diesel fumes and asthmagens. \ud Results We assigned 186 (53%) of all SOC codes as exposed to at least one category of VGDFFiM, with 23% assigned as having medium or high exposure. We assigned over 68% of all codes as not being exposed to fibres, gases or mists. The most common exposure was to dusts (22% of codes with >50% exposed); 12% of codes were assigned exposure to fibres. We assigned higher percentages of the codes as exposed to diesel fumes (14%) compared with metals (8%). Conclusions We developed an expert-derived JEM, using a strict set of a priori defined rules. The ACE JEM could also be applied to studies to assess risks of diseases where the main route of occupational exposure is via inhalation