43 research outputs found
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
Expeditious production of concentrated glucose-rich hydrolysate from sugarcane bagasse and its fermentation to lactic acid with high productivity
Sugarcane bagasse (SCB) is anticipated to emerge as a potential threat to waste management in India on account of cheap surplus energy options and lower incentives through its co-generation. Through biotechnological intervention, the efficient utilization of SCB is seen as an opportunity. The present study aimed towards expeditious production of concentrated glucose-rich hydrolysate from SCB. Alkali pretreated biomass was chosen for hydrolysis with a new generation cellulase cocktail, Cellic CTec2 dosed at 25 mg g−1 glucan content. A two-step (9% + 9%) substrate feeding strategy was adopted with a gap of an hour, and saccharification was terminated in three different ways. Irrespective of the methods employed for termination, ∼84.5% cellulose was hydrolyzed releasing ≥100 g L−1 glucose from 18% biomass. Direct use of glucose-rich filtrates yielded 69.2 ± 2.5 g L−1 of L (+) lactic acid (LA) using thermophilic Bacillus coagulans NCIM 5648. The best-attained glucose and LA productivities during separate hydrolysis and fermentation (SHF) in the present study were 5.27 and 2.88 g L−1 h−1, respectively. A green and sustainable process is demonstrated for the production of industrially relevant sugars from SCB at high productivity and its valorization to bio-based LA
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