Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available

Enumeration of Family Fabaceae from Sechu Tuan Nalla Wildlife Sanctuary, Chamba District, Himachal Pradesh (India)

An account of 20 species under 11 genera of the family Fabaceae is presented based upon a thorough study of the collected specimens and field surveys in this paper from Sechu Tuan Nalla Wildlife Sanctuary, Chamba district, Himachal Pradesh. Of these, fourteen taxa are reported first time from the Chamba district of the state. The updated nomenclature of the species, local name if any, a brief description of the plant, flowering and fruiting period, distribution in the study area, habitat and ecology and specimen examined have been provided

ANTI-ANGIOGENIC ACTIVITY OF THE EXTRACTED FERMENTATION BROTH OF AN ENTOMOPATHOGENIC FUNGUS, CORDYCEPS MILITARIS 3936

Objective: Cordyceps militaris is an entomopathogen and known to exhibit significant therapeutic potential. In the present study, we aimed to extract various fractions (aqueous; hexane; chloroform & butanol) including active ingredient cordycepin from fermented broth of Cordyceps militaris followed by their evaluation as anti-angiogenic agents. Methods: The bioactive metabolite, cordycepin and various Cordyceps derived fractions were isolated from liquid culture of Cordyceps militaris using solvent-solvent extraction method followed by purification on silica gel column chromatography. Furthermore anti-angiogenic properties of extracted fermentation broth were also investigated using chorioallantoic membrane (CAM) assay. Results: Butanolic fractions, demonstrated the highest anti-angiogenic activity followed by chloroform, hexane and aqueous fractions of extracted fermentation broth. Anti-angiogenic studies for extracted cordycepin showed that 40 µg/egg dosage of cordycepin was sufficient to inhibit the branching of blood vessels significantly (~50%) in a CAM assay. Conclusion: It is concluded that butanolic extract/cordycepin from fermented broth of Cordyceps militaris potentially inhibits the angiogenesis and suggests that the inhibition of angiogenesis is one of the mechanisms by which Cordyceps militaris can mediate an anti-cancer effect

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

Exciting new work on the generalization bounds for neural networks (NN) given by Neyshabur et al. , Bartlett et al. closely depend on two parameter-depenedent quantities: the Lipschitz constant upper-bound and the stable rank (a softer version of the rank operator). This leads to an interesting question of whether controlling these quantities might improve the generalization behaviour of NNs. To this end, we propose stable rank normalization (SRN), a novel, optimal, and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator. Surprisingly we find that SRN, inspite of being non-convex problem, can be shown to have a unique optimal solution. Moreover, we show that SRN allows control of the data-dependent empirical Lipschitz constant, which in contrast to the Lipschitz upper-bound, reflects the true behaviour of a model on a given dataset. We provide thorough analyses to show that SRN, when applied to the linear layers of a NN for classification, provides striking improvements-11.3% on the generalization gap compared to the standard NN along with significant reduction in memorization. When applied to the discriminator of GANs (called SRN-GAN) it improves Inception, FID, and Neural divergence scores on the CIFAR 10/100 and CelebA datasets, while learning mappings with low empirical Lipschitz constants.Comment: Accepted at the International Conference in Learning Representations, 2020, Addis Ababa, Ethiopi

Continual Learning in Low-rank Orthogonal Subspaces

In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experience is finished. The prior art in CL uses episodic memory, parameter regularization or extensible network structures to reduce interference among tasks, but in the end, all the approaches learn different tasks in a joint vector space. We believe this invariably leads to interference among different tasks. We propose to learn tasks in different (low-rank) vector subspaces that are kept orthogonal to each other in order to minimize interference. Further, to keep the gradients of different tasks coming from these subspaces orthogonal to each other, we learn isometric mappings by posing network training as an optimization problem over the Stiefel manifold. To the best of our understanding, we report, for the first time, strong results over experience-replay baseline with and without memory on standard classification benchmarks in continual learning. The code is made publicly available.Comment: The paper is accepted at NeurIPS'2