597 research outputs found
DEEP FULLY RESIDUAL CONVOLUTIONAL NEURAL NETWORK FOR SEMANTIC IMAGE SEGMENTATION
Department of Computer Science and EngineeringThe goal of semantic image segmentation is to partition the pixels of an image into semantically meaningful parts and classifying those parts according to a predefined label set. Although object recognition
models achieved remarkable performance recently and they even surpass human???s ability to recognize
objects, but semantic segmentation models are still behind. One of the reason that makes semantic
segmentation relatively a hard problem is the image understanding at pixel level by considering global
context as oppose to object recognition. One other challenge is transferring the knowledge of an object
recognition model for the task of semantic segmentation. In this thesis, we are delineating some of the
main challenges we faced approaching semantic image segmentation with machine learning algorithms.
Our main focus was how we can use deep learning algorithms for this task since they require the
least amount of feature engineering and also it was shown that such models can be applied to large scale
datasets and exhibit remarkable performance. More precisely, we worked on a variation of convolutional
neural networks (CNN) suitable for the semantic segmentation task. We proposed a model called deep
fully residual convolutional networks (DFRCN) to tackle this problem. Utilizing residual learning makes
training of deep models feasible which ultimately leads to having a rich powerful visual representation.
Our model also benefits from skip-connections which ease the propagation of information from the
encoder module to the decoder module. This would enable our model to have less parameters in the
decoder module while it also achieves better performance. We also benchmarked the effective variation
of the proposed model on a semantic segmentation benchmark.
We first make a thorough review of current high-performance models and the problems one might
face when trying to replicate such models which mainly arose from the lack of sufficient provided
information. Then, we describe our own novel method which we called deep fully residual convolutional
network (DFRCN). We showed that our method exhibits state of the art performance on a challenging
benchmark for aerial image segmentation.clos
Tensor Products of Some Special Rings
In this paper we solve a problem, originally raised by Grothendieck, on the
properties, i.e. Complete intersection, Gorenstein, Cohen--Macaulay, that are
conserved under tensor product of algebras over a field .Comment: 6 page
On the notion of Cohen-Macaulayness for non Noetherian rings
There exist many characterizations of Noetherian Cohen-Macaulay rings in the
literature. These characterizations do not remain equivalent if we drop the
Noetherian assumption. The aim of this paper is to provide some comparisons
between some of these characterizations in non Noetherian case. Toward solving
a conjecture posed by Glaz, we give a generalization of the Hochster-Eagon
result on Cohen-Macaulayness of invariant rings, in the context of non
Noetherian rings.Comment: 2
Gorenstein homological dimensions and Auslander categories
In this paper, we study Gorenstein injective, projective, and flat modules
over a Noetherian ring . For an -module , we denote by
and the Gorenstein projective and flat dimensions of ,
respectively. We show that if and only if provided the Krull dimension of is finite. Moreover, in the
case that is local, we correspond to a dualizing complex of
, the classes and of -modules. For a module
over a local ring , we show that if and only if or equivalently . In dual situation by
using the class , we provide a characterization of Gorenstein injective
modules.Comment: 15 page
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