Decoder
3527 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Decoder models and implementationsMost implemented papers
Attention Is All You Need
tensorflow/tensor2tensor
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NeurIPS 2017
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Neural Machine Translation by Jointly Learning to Align and Translate
Neural machine translation is a recently proposed approach to machine translation.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
tensorflow/models
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ECCV 2018
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Searching for MobileNetV3
tensorflow/models
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ICCV 2019
We achieve new state of the art results for mobile classification, detection and segmentation.
Masked Autoencoders Are Scalable Vision Learners
facebookresearch/mae
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CVPR 2022
Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels.
Neural Discrete Representation Learning
deepmind/sonnet
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NeurIPS 2017
Learning useful representations without supervision remains a key challenge in machine learning.
High Quality Monocular Depth Estimation via Transfer Learning
Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN).
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
huggingface/transformers
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ACL 2020
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
