Sequential Image Classification
38 papers with code • 3 benchmarks • 3 datasets
Sequential image classification is the task of classifying a sequence of images.
( Image credit: TensorFlow-101 )
Benchmarks
These leaderboards are used to track progress in Sequential Image Classification
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Libraries
Use these libraries to find Sequential Image Classification models and implementations
CIFAR-10
MNIST
Mudestreda
Most implemented papers
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
locuslab/TCN
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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.
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Sunnydreamrain/IndRNN_Theano_Lasagne
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CVPR 2018
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.
Resurrecting Recurrent Neural Networks for Long Sequences
Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are hard to optimize and slow to train.
Efficiently Modeling Long Sequences with Structured State Spaces
A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies.
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients.
Recurrent Highway Networks with Grouped Auxiliary Memory
WilliamRo/gam_rhn
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IEEE Access 2019
In this paper, we address these issues by proposing a novel RNN architecture based on RHN, namely the Recurrent Highway Network with Grouped Auxiliary Memory (GAM-RHN).
Recurrent Batch Normalization
cooijmanstim/recurrent-batch-normalization
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We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks.
Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients.
Unitary Evolution Recurrent Neural Networks
v0lta/Complex-gated-recurrent-neural-networks
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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.
Full-Capacity Unitary Recurrent Neural Networks
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.
