Permuted-MNIST
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Permuted-MNIST
Most implemented papers
Three scenarios for continual learning
GMvandeVen/continual-learning
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Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning.
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
jingli9111/EUNN-tensorflow
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ICML 2017
Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn long-term correlations in the data.
Generative replay with feedback connections as a general strategy for continual learning
GMvandeVen/continual-learning
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A major obstacle to developing artificial intelligence applications capable of true lifelong learning is that artificial neural networks quickly or catastrophically forget previously learned tasks when trained on a new one.
Low-rank passthrough neural networks
Various common deep learning architectures, such as LSTMs, GRUs, Resnets and Highway Networks, employ state passthrough connections that support training with high feed-forward depth or recurrence over many time steps.
HiPPO: Recurrent Memory with Optimal Polynomial Projections
HazyResearch/hippo-code
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NeurIPS 2020
A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed.
IGLOO: Slicing the Features Space to Represent Sequences
hello1910/eeg
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One notable issue is the relative difficulty to deal with long sequences (i. e. more than 20, 000 steps).
Improving and Understanding Variational Continual Learning
nvcuong/variational-continual-learning
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In the continual learning setting, tasks are encountered sequentially.
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization
AntonioCarta/rnn_autoencoding_neurips2020
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Training RNNs to learn long-term dependencies is difficult due to vanishing gradients.
CKConv: Continuous Kernel Convolution For Sequential Data
Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be defined a priori.
Shared and Private VAEs with Generative Replay for Continual Learning
We propose a hybrid continual learning model that is more suitable in real case scenarios to address the issues that has a task-invariant shared variational autoencoder and T task-specific variational autoencoders.
