Results 1 to 10 of about 5,327,787 (369)
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning [PDF]
AAAI Conference on Artificial Intelligence, 2016 Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational ...
Alemi, Alex +3 more
core +2 more sources
Fate of Iprobenfos and Tricyclazole at Paddy Cultivation Environment [PDF]
대한환경공학회지, 2023 Objectives This study aimed to identify the fate of iprobenfos and tricyclazole in the soil and paddy water during the rice cultivation process and to identify their exposure pathways into surface water.
Hyosub Lee +4 more
doaj +1 more source
Autoregressive Image Generation using Residual Quantization [PDF]
Computer Vision and Pattern Recognition, 2022 For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range ...
Doyup Lee +4 more
semanticscholar +1 more source
Scientific Reports, 2021 Tenebrio molitor larva (mealworms) has recently attracted attention as a protein source for food and feed. The larva is generally fed with wheat bran, which can be possibly contaminated with glyphosate.
Leesun Kim +6 more
doaj +1 more source
Monitoring and risk analysis of residual pesticides drifted by unmanned aerial spraying
Scientific Reports, 2023 This study aimed to investigate the residual characteristics of pesticides drifted by unmanned aerial spray according to buffer strip, windbreak, and morphological characteristics of non-target crops, suggest prevention for drift reduction, and finally ...
Chang Jo Kim +4 more
doaj +1 more source
Enhanced Deep Residual Networks for Single Image Super-Resolution [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance.
Bee Lim +4 more
semanticscholar +1 more source
British Machine Vision Conference, 2016 Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual ...
Sergey Zagoruyko, N. Komodakis
semanticscholar +1 more source
Foods, 2022 Since the introduction of the positive list system (PLS) for agricultural products in the Republic of Korea, the demand for a quick, easy multi-residue analysis method increased continuously.
Xiu Yuan +9 more
doaj +1 more source
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising [PDF]
IEEE Transactions on Image Processing, 2016 The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
K. Zhang +4 more
semanticscholar +1 more source
Foods, 2023 The significance of sample grinding is frequently disregarded during the development of analytical methods, which are often validated with spiked samples that may not accurately reflect incurred residues.
Xiu Yuan, Chang Jo Kim, Hyun Ho Noh
doaj +1 more source