Browsing by Author "Jain, M."

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A Systematic Mapping Study of Content Based Filtering Recommender Systems
(Springer Science and Business Media Deutschland GmbH, 2019) Jain, M.; Singh, S.; Chandrasekaran, K.
There has been extremely limited use of recommender systems for clothing suggestions. A clear idea of where recommender systems are used would facilitate the correct method of implementation for the domain given above. In order to propose a solution, there is a need to properly analyse the various existing approaches and solutions developed in a particular field. This study will help us gain clarity to answer several research questions in the chosen domain. A systematic mapping study is carried out to identify as well as classify the research papers pertaining to the chosen field. © 2019, Springer Nature Switzerland AG.
Capsule Network–based architectures for the segmentation of sub-retinal serous fluid in optical coherence tomography images of central serous chorioretinopathy
(Springer Science and Business Media Deutschland GmbH, 2021) Pawan, S.J.; Sankar, R.; Jain, A.; Jain, M.; Darshan, D.V.; Anoop, B.N.; Kothari, A.R.; Venkatesan, M.; Rajan, J.
Central serous chorioretinopathy (CSCR) is a chorioretinal disorder of the eye characterized by serous detachment of the neurosensory retina at the posterior pole of the eye. CSCR results from the accumulation of subretinal fluid (SRF) due to idiopathic defects at the level of the retinal pigment epithelial (RPE) that allows serous fluid from the choriocapillaris to diffuse into the subretinal space between RPE and neurosensory retinal layers. This condition is presently investigated by clinicians using invasive angiography or non-invasive optical coherence tomography (OCT) imaging. OCT images provide a representation of the fluid underlying the retina, and in the absence of automated segmentation tools, currently only a qualitative assessment of the same is used to follow the progression of the disease. Automated segmentation of the SRF can prove to be extremely useful for the assessment of progression and for the timely management of CSCR. In this paper, we adopt an existing architecture called SegCaps, which is based on the recently introduced Capsule Networks concept, for the segmentation of SRF from CSCR OCT images. Furthermore, we propose an enhancement to SegCaps, which we have termed as DRIP-Caps, that utilizes the concepts of Dilation, Residual Connections, Inception Blocks, and Capsule Pooling to address the defined problem. The proposed model outperforms the benchmark UNet architecture while reducing the number of trainable parameters by 54.21%. Moreover, it reduces the computation complexity of SegCaps by reducing the number of trainable parameters by 37.85%, with competitive performance. The experiments demonstrate the generalizability of the proposed model, as evidenced by its remarkable performance even with a limited number of training samples. [Figure not available: see fulltext.]. © 2021, International Federation for Medical and Biological Engineering.
DROCC: Deep Robust One-Class Classification
(ML Research Press, 2020) Goyal, S.; Raghunathan, A.; Jain, M.; Simhadri, H.; Jain, P.
Classical approaches for one-class problems such as one-class SVM and isolation forest require careful feature engineering when applied to structured domains like images. State-of-the-art methods aim to leverage deep learning to learn appropriate features via two main approaches. The first approach based on predicting transformations (Golan & El-Yaniv, 2018; Hendrycks et al., 2019a) while successful in some domains, crucially depends on an appropriate domain-specific set of transformations that are hard to obtain in general. The second approach of minimizing a classical one-class loss on the learned final layer representations, e.g., DeepSVDD (Ruff et al., 2018) suffers from the fundamental drawback of representation collapse. In this work, we propose Deep Robust One Class Classification (DROCC) that is both applicable to most standard domains without requiring any side-information and robust to representation collapse. DROCC is based on the assumption that the points from the class of interest lie on a well-sampled, locally linear low dimensional manifold. Empirical evaluation demonstrates that DROCC is highly effective in two different one-class problem settings and on a range of real-world datasets across different domains: tabular data, images (CIFAR and ImageNet), audio, and time-series, offering up to 20% increase in accuracy over the state-of-the-art in anomaly detection. Code is available at https://github.com/microsoft/EdgeML. Â© 2020 by the author(s).
DROCC: Deep robust one-class classification
(International Machine Learning Society (IMLS), 2020) Goyal, S.; Raghunathan, A.; Jain, M.; Simhadri, H.; Jain, P.
Classical approaches for one-class problems such as one-class SVM and isolation forest require careful feature engineering when applied to structured domains like images. State-of-the-art methods aim to leverage deep learning to learn appropriate features via two main approaches. The first approach based on predicting transformations (Golan & El-Yaniv, 2018; Hendrycks et al., 2019a) while successful in some domains, crucially depends on an appropriate domain-specific set of transformations that are hard to obtain in general. The second approach of minimizing a classical one-class loss on the learned final layer representations, e.g., DeepSVDD (Ruff et al., 2018) suffers from the fundamental drawback of representation collapse. In this work, we propose Deep Robust One Class Classification (DROCC) that is both applicable to most standard domains without requiring any side-information and robust to representation collapse. DROCC is based on the assumption that the points from the class of interest lie on a well-sampled, locally linear low dimensional manifold. Empirical evaluation demonstrates that DROCC is highly effective in two different one-class problem settings and on a range of real-world datasets across different domains: tabular data, images (CIFAR and ImageNet), audio, and time-series, offering up to 20% increase in accuracy over the state-of-the-art in anomaly detection. Code is available at https://github.com/microsoft/EdgeML. Â© 2020 by the author(s).
Improving convergence in Irgan with PPO
(2020) Jain, M.; Sowmya, Kamath S.
Information retrieval modeling aims to optimise generative and discriminative retrieval strategies, where, generative retrieval focuses on predicting query-specific relevant documents and discriminative retrieval tries to predict relevancy given a query-document pair. IRGAN unifies the generative and discriminative retrieval approaches through a minimax game. However, training IRGAN is unstable and varies largely with the random initialization of parameters. In this work, we propose improvements to IRGAN training through a novel optimization objective based on proximal policy optimisation and gumbel-softmax based sampling for the generator, along with a modified training algorithm which performs the gradient update on both the models simultaneously for each training iteration. We benchmark our proposed approach against IRGAN on three different information retrieval tasks and present empirical evidence of improved convergence. � 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Improving convergence in Irgan with PPO
(Association for Computing Machinery, 2020) Jain, M.; Kamath Sâ€¤, S.
Information retrieval modeling aims to optimise generative and discriminative retrieval strategies, where, generative retrieval focuses on predicting query-specific relevant documents and discriminative retrieval tries to predict relevancy given a query-document pair. IRGAN unifies the generative and discriminative retrieval approaches through a minimax game. However, training IRGAN is unstable and varies largely with the random initialization of parameters. In this work, we propose improvements to IRGAN training through a novel optimization objective based on proximal policy optimisation and gumbel-softmax based sampling for the generator, along with a modified training algorithm which performs the gradient update on both the models simultaneously for each training iteration. We benchmark our proposed approach against IRGAN on three different information retrieval tasks and present empirical evidence of improved convergence. Â© 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe
(Institute of Electrical and Electronics Engineers Inc., 2020) Jain, M.; Singh, S.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.
In the present-day scenario, several clothing recommender systems have been developed for the online e-commerce industry. However, when it comes to recommending clothes that a person already possesses, i.e, from their personal wardrobe, there are very few systems that have been proposed to perform the task. In this paper, we tackle the latter issue, and perform experimental analysis of the various Machine Learning techniques that can be used for carrying out the task. Since the recommendations must be made from a user's personal wardrobe, the recommender system doesn't follow a traditional approach. This is explained in detail in the following sections. Further, the paper contains a complete description of the results obtained from the experiments conducted, and the best approach is specified, with appropriate justification for the same. Â© 2020 IEEE.