Deep Learning, NLP, NMT, AI, ML
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK REMOVE Object Detection COCO test-dev GFLV2 (ResNet-50) box AP 44.3 # 45 Object Detection COCO test-dev GFLV2 (ResNet-50) AP50 62.3 # 54 Object Detection COCO test-dev GFLV2 (ResNet-50) AP75 48.5 # 47 Object Detection COCO test-dev GFLV2 (ResNet-50) APS 26.8
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This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlooked at best, whether not undeveloped… The methodological rationale behind the framework we propose is that in order to study, develop and compare new rPPG methods in a principled and reproducible way, the following […]
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Introduction Matplotlib is one of the most widely used data visualization libraries in Python. Much of Matplotlib’s popularity comes from its customization options – you can tweak just about any element from its hierarchy of objects. In this tutorial, we’ll take a look at how to change the marker size in a Matplotlib scatter plot. Import Data We’ll use the World Happiness dataset, and compare the Happiness Score against varying features to see what influences perceived happiness in the world: […]
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Introduction Lists are one of the four most commonly used data structures provided by Python. Its functionality, extensibility, and ease of use make it useful for implementing various types of functionalities. Python lists have a few interesting characteristics: Mutability – meaning it can change, which means it allows us to easily add and delete entries from it. This is the main difference between Python lists and tuples Homogeneity – meaning all elements within one list have to be of the […]
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Introduction Matplotlib is one of the most widely used data visualization libraries in Python. From simple to complex visualizations, it’s the go-to library for most. In this tutorial, we’ll take a look at how to plot a histogram plot in Matplotlib. Histogram plots are a great way to visualize distributions of data – In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range. A histogram displays the shape and spread of […]
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Introduction In this article, we’ll take a look at how to convert Bytes to a String in Python. By the end of this article you will have a clear idea of what these types are and how to effectively handle data using them. Depending on the version of Python you’re using, this task will differ. Although Python 2 has reached its end of life, many projects still use it, so we’ll include both the Python 2 and Python 3 approaches. […]
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This article was published as a part of the Data Science Blogathon. Introduction NLTK is a string processing library that takes strings as input. The output is in the form of either a string or lists of strings. This library provides a lot of algorithms that helps majorly in the learning purpose. One can compare among different variants of outputs. There are other libraries as well like spaCy, CoreNLP, PyNLPI, Polyglot. NLTK and spaCy are most widely used. Spacy works […]
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Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task. In this work, we propose exploiting Latent Geometry Graphs (LGGs) to represent the latent spaces of trained DNN architectures… Such graphs are obtained by connecting samples that yield similar latent representations at a given layer of the considered DNN. We then obtain a generalization score by looking at how strongly connected are samples of distinct classes in LGGs. This score […]
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Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable performance… Unfortunately, training such systems requires several hours of GPU time. For robots, to successfully adapt to changes in the environment or learning new objects, it is also important that object detectors can be re-trained in a short amount of time. A recent method [1] proposes an architecture that leverages on the powerful representation […]
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Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of primary interest. In computer vision, learning from long tailed datasets is a recurring theme, especially for natural image datasets… While existing solutions mostly appeal to sampling or weighting adjustments to alleviate the pathological imbalance, or imposing inductive bias to prioritize non-spurious associations, we take novel perspectives to promote sample efficiency and model generalization based on the […]
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