In this paper, we present FADE, a novel, plug-and-play, lightweight, and task-agnostic upsampling operator by fusing the assets of decoder and encoder features at three levels: (i) considering both the encoder and decoder feature in upsampling kernel generation; (ii) controlling the per-...
It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. SegNet的灵感来源于场景理解应用。因此,它能有效地在推理期间减少内存占用和增加计算效率。与其他model相比,它参数数量也要少得多,并且...
Models like BERT and T5 are trained with an encoder only orencoder-decoderarchitectures. These models have demonstrated near-universal state of the art performance across thousands of natural language tasks. That said, the downside of such models is that they require a significant number of task-...
Cipolla. Bayesian segnet: Model uncertainty in deep convolutional encoder- decoder architectures for scene understanding. arXiv, 2015.Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segnet: Model uncer- tainty in deep convolutional encoder-decoder architectures for scene understanding. CoRR ...
NVIDIA recently announced that NVIDIA TensorRT-LLM now accelerates encoder-decoder model architectures. TensorRT-LLM is an open-source library that optimizes…
Image captioning with a benchmark of CNN-based encoder and GRU-based inject-type (init-inject, pre-inject, par-inject) and merge decoder architectures natural-language-processingimage-processingpytorchimage-captioningconvolutional-neural-networksinception-v3gated-recurrent-unitsencoder-decoder-architecture ...
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文章目录 Representation Types and Architectures Pre-training Task Types Extentions of PTMs Adapting PTMs to Downstream Tasks Future Direction 转载来源:https://zhuanlan.zhihu.com/p/139015428 作者:徐阿衡 Pre-trained... NLP预训练模型综述 现在深度学习越来越火了,在NLP领域主要使用CNNs、RNNs、GNNs以及att...
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling
In this work, we develop a new modular deep encoder–decoder hierarchical (DeepEDH) convolutional neural network architecture based on image-to-image regression, which builds on previous DenseED [44] and fully convolutional DenseNet [45] architectures. Our DeepEDH architecture is tailored for surroga...