Exploring Simple Siamese Representation Learning Siamese network is two parallel network sharing same weights to maximize the similarity between each network input. Siamese network is trained in an unsupervised manner. A image is augmented to create a positive pair for which the similarity score is ideally 1, on the other hand, a negativ... Read more 23 Nov 2020 - 1 minute read
Paper This paper presents a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. The source knowledge networks are called teacher model, and the learning network is called student network. Adversari... Read more 04 Oct 2020 - 6 minute read
The loss functions are the major driving force in training a good model. In Object detection and Instance segmentation tasks, the most widely used loss function is Intersection over Union (IOU). In Enhancing Geometric Factors for Object Detection and Instance Segmentation Loss function. paper, a new loss function called as Complete Intersection ... Read more 09 May 2020 - 4 minute read
Named Entity Recognition is the process of extracting entities present in sentences. This is an important aspect in information extraction from text documents. NER can also be used in conversational chats to extract entities such as location, time and many more. NER can also be trained to extract person names in information extraction of legal... Read more 12 Apr 2020 - 17 minute read
Text Summarizing is an important NLP Task. It comprises of two categories, abstractive summarization and extractive summarization. However, the real world application of text summarization is challenging due to human readability and quality. I will try to explain different research approaches and architectures employed. This blog post will foc... Read more 29 Feb 2020 - 11 minute read
CenterNet: Keypoint Triplets for Object Detection This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather ... Read more 02 Dec 2019 - 6 minute read
Paper: An overview of gradient descent optimization algorithms This article will discuss about different optimization techniques that are widely employed in Neural Networks. This article focuses on optimizer selection strategy for desired Neural Networks. Gradient Descent Gradient descent is a way to minimize an objective function J(θ) pa... Read more 24 Nov 2019 - 12 minute read
Paper: EfficientDet This paper discusses about the optimized approach in Image detection tasks and compares it with other detection models such as YOLOv3, MaskRCNN and many more by building a scalable detection architecture. It proposes a weighted Bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature ... Read more 22 Nov 2019 - 4 minute read
This blog provides indepth understanding of optimization from Introduction to deep learning course by CMU . Course Page In neural networks, the convergence is attained by finding the optimal network error. The error is defined as a function called the Loss function, which must be minimized through an iterative process of weight updates... Read more 28 Oct 2019 - 1 minute read