Regularizing deep networks using efficient layerwise adversarial training

Adversarial training has been shown to regularize deep neural networks in addition to increasing their robustness to adversarial examples. However, its impact on very deep state of the art networks has not been fully investigated. In this paper, we present an efficient approach to perform adversarial training by perturbing intermediate layer activations and study the use of such perturbations as a regularizer during training. We use these perturbations to train very deep models such as ResNets and show improvement in performance both on adversarial and original test data. Our experiments highlight the benefits of perturbing intermediate layer activations compared to perturbing only the inputs. The results on CIFAR-10 and CIFAR-100 datasets show the merits of the proposed adversarial training approach. Additional results on WideResNets show that our approach provides significant improvement in classification accuracy for a given base model, outperforming dropout and other base models of larger size.Comment: Published at the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). Official link: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/1663

Generate To Adapt: Aligning Domains using Generative Adversarial Networks

Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.Comment: Accepted as spotlight talk at CVPR 2018. Code available here: https://github.com/yogeshbalaji/Generate_To_Adap

Directional Correlation Study of Gamma Cascades in the Decay of Sb124

The delay scheme of sb124 studied and the gamma-gamma directional correlation measurements are carried out for few cascades.On the basis of the experimental data on directional correlations, the spin assignments are made for the 603, 1326, 1964, 2313, 2688 keV excited levels of Te124. Multipole assignments are made for 989, 1362 keV transitions

RANK Signaling Amplifies WNT-Responsive Mammary Progenitors through R-SPONDIN1

SummarySystemic and local signals must be integrated by mammary stem and progenitor cells to regulate their cyclic growth and turnover in the adult gland. Here, we show RANK-positive luminal progenitors exhibiting WNT pathway activation are selectively expanded in the human breast during the progesterone-high menstrual phase. To investigate underlying mechanisms, we examined mouse models and found that loss of RANK prevents the proliferation of hormone receptor-negative luminal mammary progenitors and basal cells, an accompanying loss of WNT activation, and, hence, a suppression of lobuloalveologenesis. We also show that R-spondin1 is depleted in RANK-null progenitors, and that its exogenous administration rescues key aspects of RANK deficiency by reinstating a WNT response and mammary cell expansion. Our findings point to a novel role of RANK in dictating WNT responsiveness to mediate hormone-induced changes in the growth dynamics of adult mammary cells

महामानवरूप-विश्वपुरुष गुरु गोविन्द सिंह / लेखक, रणधीर सिंह

application/pdfIn Hind