Sequence & generative
103 methods in this family.
Featured
Attention MechanismThe attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of theAutoencoderAn autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs Bidirectional RNNA Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surroundCrossformerCrossformer is a Transformer-based architecture for multivariate time series forecasting, introduced by Yunhao Zhang and Junchi Yan at ICLR 2023. Unlike earlier Transformer variantCycleGANCycleGAN, introduced by Zhu et al. at ICCV 2017, learns to translate images between two visual domains without requiring paired training examples. It trains two generators and two DeepARDeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estima
All methods 103
Attention MechanismAutoencoderBidirectional RNNCrossformerCycleGANDeepARDiffusion ModelDomain-adaptive diffusion modelDomain-adaptive GANDomain-adaptive GRUDomain-adaptive Recurrent Neural NetworkDomain-adaptive sentence embeddingsDomain-adaptive transformerDomain-adaptive variational autoencoderDomain-adaptive vision transformerExplainable Diffusion ModelExplainable GANExplainable GRUExplainable LSTMExplainable Recurrent Neural NetworkExplainable TransformerExplainable Variational AutoencoderFEDformerFine-Tuned Diffusion ModelFine-Tuned Generative Adversarial NetworkFine-Tuned GRUFine-Tuned LSTMFine-Tuned Recurrent Neural NetworkFine-Tuned Text SummarizationFine-Tuned TransformerFine-Tuned Variational AutoencoderFine-Tuned Vision TransformerGated Recurrent UnitGenerative Adversarial NetworkGraph Attention NetworkGRUInformeriTransformerLatent Diffusion ModelsLong Short-Term MemoryLongformer / BigBirdLSTMMasked AutoencodersMoiraiMultilingual Diffusion ModelMultilingual GANMultilingual GRUMultilingual LSTMMultilingual Recurrent Neural NetworkMultilingual text summarizationMultilingual variational autoencoderMultilingual vision transformerMultimodal Diffusion ModelMultimodal GANMultimodal GRUMultimodal LSTMMultimodal Recurrent Neural NetworkMultimodal TransformerMultimodal Variational AutoencoderMultimodal Vision TransformerNon-stationary TransformerPatchTSTPyraformerRecurrent Neural NetworkReformerScore-Based Generative ModelSegRNNSelf-AttentionSelf-supervised Diffusion ModelSelf-supervised GANSelf-supervised GRUSelf-supervised TransformerSelf-supervised Variational AutoencoderSelf-supervised Vision TransformerSemi-supervised Diffusion ModelSemi-supervised GANSemi-supervised GRUSemi-supervised LSTMSemi-supervised TransformerSemi-supervised Variational AutoencoderSemi-supervised Vision TransformerSequence-to-Sequence ModelSwin TransformerT5 (Text-to-Text Transfer Transformer)Temporal Fusion TransformerTime-MoETiRexTransfer learning GANTransfer learning variational autoencoderTransfer Learning with Diffusion ModelTransfer Learning with LSTMTransfer Learning with Recurrent Neural NetworkVariational AutoencoderVision TransformerWasserstein GANWeakly Supervised Diffusion ModelWeakly supervised GANWeakly Supervised GRUWeakly supervised LSTMWeakly supervised recurrent neural networkWeakly supervised transformerWeakly Supervised Variational AutoencoderWeakly supervised vision transformer