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Papers with code3/28/2023 Although ViTs showed great performance and gained popularity, ViTs got issues when applied to general computer vision tasks like objection detection and semantic segmentation. The ViTs were found to overthrow ConNets or CNNs, the state-of-the-art image classification models in the 20s. The “A ConvNet for the 2020s” paper revolves around visual recognition tasks and introduces vision transformers or ViTs. Read more: Harvard psychologist identifies machine learning approach to human psychology 4. Link to the paper: Visual Attention Network This paper shows that VANs outperform ViTs and CNNs in extensive experiments for tasks like image classification, semantic segmentation, pose estimation, and more. The research is an attention VAN based on LKA, which is similar to ViTs and convolutional neural networks (CNNs). This research paper is authored by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, and Shi-Min Hu. LKA enables the self-adaptive and long-range correlations in self-attention while avoiding the three shortcomings: training images as 1D sequences neglecting their 2D structures, quadratic complexity is too expensive, and capturing only spatial adaptability but ignores channel adaptability. “Visual Attention Network” paper proposes a novel linear attention named large kernel attention (LKA). Link to the paper: A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation Additionally, the paper proposes a simple and compact ViT architecture called Universal Vision Transformer (UViT) to achieve high performance on common objects in context (COCO) object detection and instance segmentation tasks. This paper comprises three architectural options in ViT: spatial reduction, doubled channels, and multiscale features, demonstrating that a vanilla ViT architecture can provide a better trade-off without multiscale features. The multi-stage design provides a better trade-off among computational costs and effective aggregation of multiscale global contexts. With the adaptation of vision transformer (ViT) for object detection and dense prediction tasks, many models inherited multistage designs. The “A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation” paper put forth a simple vision transformer design to use object localization and instance segmentation tasks. A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation Link to the paper: TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine LearningĬode: GitHub 2. The paper concludes by providing TensorFlow Eager that is easier to interpolate between imperative and staged execution in a single package. TensorFlow Eager provides a crucial front-end to TensorFlow used to execute operations immediately and a JIT tracker translating Python functions composed of TensorFlow operations. On the contrary, TensorFlow Eager excels TensorFlow and eliminates the usability cost without sacrificing the benefits of graphs. TensorFlow has shown remarkable performance but requires users to represent competitions as dataflow graphs, hindering rapid prototyping and run-time dynamism. “TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning” paper introduces TensorFlow Eager, a multi-stage Python-embedded domain-specific language for hardware accelerated machine learning suitable for both interactive research and production. ![]() TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning This list contains the top 10 machine learning papers available on Papers with Code. List of top machine learning papers on Papers with Code Here is the list of the popular machine learning papers on Papers with Code. ![]() Some significant methods and modules are popular for research and study in these papers. Papers with Code has 79,817 papers, 9,327 benchmarks, and 3,681 tasks till now, and we expect to see more state-of-art papers in the future. ![]() These resources are provided with the support of the natural language processing and machine learning community. The objective of the website is to create a free and open resource for machine learning and computer vision researchers, including machine learning papers, code, datasets, methods, and evaluation tables. Papers with Code is a website organizing free access to technical published papers and providing the software used in the papers. There are more open repositories, including Paper with Code, Crossminds, Connected Papers, and others. According to the data mining blog, roughly 100 machine learning papers are published in a day on Arxiv, a well-known public repository of research papers. The world now realizes the value of machine learning algorithms in computing and forecasting, which has fueled a boom in machine learning research.
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