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Use these libraries to find Decoder models and implementations
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Most implemented papers

Attention Is All You Need

tensorflow/tensor2tensor 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

graykode/nlp-tutorial 1 Sep 2014

Neural machine translation is a recently proposed approach to machine translation.

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

tensorflow/models 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

PaddlePaddle/PaddleSeg 2 Nov 2015

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 ICCV 2019

We achieve new state of the art results for mobile classification, detection and segmentation.

Masked Autoencoders Are Scalable Vision Learners

facebookresearch/mae 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 NeurIPS 2017

Learning useful representations without supervision remains a key challenge in machine learning.

High Quality Monocular Depth Estimation via Transfer Learning

ialhashim/DenseDepth 31 Dec 2018

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

graykode/nlp-tutorial 3 Jun 2014

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 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.