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Sequential image classification is the task of classifying a sequence of images.

( Image credit: TensorFlow-101 )

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Greatest papers with code

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

4 Mar 2018locuslab/TCN

Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.

LANGUAGE MODELLING MACHINE TRANSLATION MUSIC MODELING SEQUENTIAL IMAGE CLASSIFICATION

Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

CVPR 2018 TobiasLee/Text-Classification

Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.

LANGUAGE MODELLING SEQUENTIAL IMAGE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Trellis Networks for Sequence Modeling

ICLR 2019 locuslab/trellisnet

On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices.

LANGUAGE MODELLING SEQUENTIAL IMAGE CLASSIFICATION

Dilated Recurrent Neural Networks

NeurIPS 2017 code-terminator/DilatedRNN

To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.

SEQUENTIAL IMAGE CLASSIFICATION

Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks

NeurIPS 2019 abr/neurips2019

Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales.

SEQUENTIAL IMAGE CLASSIFICATION TIME SERIES TIME SERIES PREDICTION

Deep Independently Recurrent Neural Network (IndRNN)

11 Oct 2019Sunnydreamrain/IndRNN_pytorch

Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construct deep networks.

LANGUAGE MODELLING SEQUENTIAL IMAGE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Recurrent Batch Normalization

30 Mar 2016cooijmanstim/recurrent-batch-normalization

We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks.

LANGUAGE MODELLING QUESTION ANSWERING READING COMPREHENSION SEQUENTIAL IMAGE CLASSIFICATION

Full-Capacity Unitary Recurrent Neural Networks

NeurIPS 2016 stwisdom/urnn

To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix.

SEQUENTIAL IMAGE CLASSIFICATION

Unitary Evolution Recurrent Neural Networks

20 Nov 2015solgaardlab/neurophox

When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies.

SEQUENTIAL IMAGE CLASSIFICATION