We for-mulate the underlying data-generating process as a hierar-chical latent variable model, and show that under reason-able assumptions, MAE provably identifies a set of latent …

Jun 8, 2023 · We formulate the underlying data-generating process as a hierarchical latent variable model and show that under reasonable assumptions, MAE provably identifies a set of …

Feb 12, 2025 · We propose to enhance the encoder's feature extraction ability in classification tasks by leveraging the latent space of variational autoencoder (VAE) and further replace it …

Specifically, we design an asymmetric extractor-decoder, where the extractor is used to learn the latent representation of the masked signals and the decoder as light as a convolution layer …

Feb 15, 2023 · The first one applies the denoising principle as well, namely, a ‘masked’ performance vector on new problems is generated and feed it into the DAE network; the …

Jun 7, 2023 · We formulate the underlying data-generating process as a hierarchical latent variable model and show that under reasonable assumptions, MAE provably identifies a set of …

Jul 8, 2024 · Masked autoencoders are neural network models designed to reconstruct input data from partially masked or corrupted versions, helping the model learn robust feature …

Nov 21, 2024 · A portion of input data is masked, and then an autoencoder is trained to recover the masked parts from the original input data. The encoder in autoencoder is encouraged to …

MAE works by identifying latent variables in the generating process! Each specific mask corresponds to a specific set of latent variables (Theorem 2). MAE can provably recover the …

feature modeling, neglecting spectral feature modeling. Mean-while, existing MIM-based methods use Transformer for feature extraction, some local or high-frequency information may get lost. …