Paper tables with annotated results for Densely Connected Search Space for More Flexible Neural Architecture Search
来自专栏 · CV每日Paper with code 2 人赞同了该文章 1.【基础网络架构】NAS-BNN: Neural Architecture Search for Binary Neural Networks 论文地址:arxiv.org//pdf/2408.154 开源代码:github.com/VDIGPKU/NAS- 2.【视频分割】Unleashing the Temporal-Spatial Reasoning Capacity of GPT for Training-Free Au...
1.【基础网络架构】SalNAS: Efficient Saliency-prediction Neural Architecture Search with self-knowledge distillation 论文地址:https://arxiv.org//pdf/2407.20062 开源代码:https://github.com/chakkritt…
STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks [paper] [arxiv] [paper with code] USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting [paper] [arxiv] [paper with code] [code] Spiking Transformer with Spatial-Temporal Attention...
TitleVenueTaskCodeYearInfo Neural Architecture Search with Reinforcement Learning ICLR IC LM - 2017 details Learning to Compose Domain-Specific Transformations for Data Augmentation NeurIPS IC O - 2017 details N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning ICLR - -...
paper | code[7] DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images(DARCNN:用于生物医学图像中无监督实例分割的基于域自适应区域的卷积神经网络) paper[6] Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient...
Figure 2: Using an Event Structure in Producer/Consumer Design Pattern Note: This image is a LabVIEW snippet, which includes LabVIEW code that you can reuse in your project. To use a snippet, right-click the image, save it to your computer, and drag the file onto your LabVIEW diagram. ...
In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel...
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This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine...