Results 11 to 20 of about 2,415,605 (342)
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
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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
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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
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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 ...
Christian Szegedy +3 more
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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
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Deep Residual Learning for Image Recognition [PDF]
Computer Vision and Pattern Recognition, 2015 Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
Kaiming He +3 more
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Aggregated Residual Transformations for Deep Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2016 We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology.
Saining Xie +4 more
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ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement [PDF]
IEEE International Conference on Computer Vision, 2021 Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its corresponding latent code of the trained GAN is of utmost importance as it ...
Yuval Alaluf, Or Patashnik, D. Cohen-Or
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Residual Dense Network for Image Super-Resolution [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well.
Yulun Zhang +4 more
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Residual Attention Network for Image Classification [PDF]
Computer Vision and Pattern Recognition, 2017 In this work, we propose Residual Attention Network, a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion.
Fei Wang +7 more
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