Attending to Discriminative Certainty for Domain Adaptation

In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approaches have focused on the entire image based domain adaptation. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in nature. To obtain such regions, we propose methods that consider the probabilistic certainty estimate of various regions and specify focus on these during classification for adaptation. We observe that just by incorporating the probabilistic certainty of the discriminator while training the classifier, we are able to obtain state of the art results on various datasets as compared against all the recent methods. We provide a thorough empirical analysis of the method by providing ablation analysis, statistical significance test, and visualization of the attention maps and t-SNE embeddings. These evaluations convincingly demonstrate the effectiveness of the proposed approach.Comment: CVPR 2019 Accepted, Project: https://delta-lab-iitk.github.io/CADA

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

Augmented hydrolysis of acid pretreated sugarcane bagasse by PEG 6000 addition: a case study of Cellic CTec2 with recycling and reuse

In an integrated lignocellulosic biorefinery, the cost associated with the “cellulases” and “longer duration of cellulose hydrolysis” represents the two most important bottlenecks. Thus, to overcome these barriers, the present study aimed towards augmented hydrolysis of acid pretreated sugarcane bagasse within a short span of 16 h using Cellic CTec2 by addition of PEG 6000. Addition of this surfactant not only enhanced glucose release by twofold within stipulated time, but aided in recovery of Cellic CTec2 which was further recycled and reused for second round of saccharification. During first round of hydrolysis, when Cellic CTec2 was loaded at 25 mg protein/g cellulose content, it resulted in 76.24 ± 2.18% saccharification with a protein recovery of 58.4 ± 1.09%. Filtration through 50KDa PES membrane retained ~ 89% protein in 4.5-fold concentrated form and leads to simultaneous fractionation of ~ 70% glucose in the permeate. Later, the saccharification potential of recycled Cellic CTec2 was assessed for the second round of saccharification using two different approaches. Unfortified enzyme effectively hydrolysed 67% cellulose, whereas 72% glucose release was observed with Cellic CTec2 fortified with 25% fresh protein top-up. Incorporating the use of the recycled enzyme in two-stage hydrolysis could effectively reduce the Cellic CTec2 loading from 25 to 16.8 mg protein/g cellulose. Furthermore, 80% ethanol conversion efficiencies were achieved when glucose-rich permeate obtained after the first and second rounds of saccharification were evaluated using Saccharomyces cerevisiae MTCC 180

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