34 research outputs found
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
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