MAE

The choice of pretraining (ImageNet-1K vs ImageNet-21K, supervised vs self-supervised like MAE/DINO) significantly impacts downstream performance, potentially causing several percentage points of difference

Score questions correspond to regression tasks or ordinal classification, and their evaluation metrics focus on MAE, RMSE, or the Spearman correlation coefficient rather than accuracy.

Mean absolute error (MAE) is a commonly used accuracy metric in prediction tasks, computed as the average of the absolute errors between predicted and true values, in milliseconds (ms).

In strict real-time backtesting, RT-LSTM achieved an average MAE of 1.405 ms across all 15 prediction horizons and 15 reference slot indices.

On 4,744 test images from the Snapshot Serengeti dataset, reconstructed at a 75% masking ratio, WildMAE achieves a PSNR of 27.90 dB, compared with 24.33 dB for the original MAE.

WildMAE proposes a decoder-side improvement scheme that achieves significant reconstruction metric gains at a 75% masking ratio.

WildMAE does not modify MAE's encoder structure; instead, it concentrates the improvements on the decoder side.

Masked autoencoders (MAE) is a self-supervised pretraining framework proposed by He et al. in 2022.

MAE divides an image into regular patches, randomly masks most of them (commonly 75%), and feeds only the visible patches into the ViT encoder.

MAE's limitation is that its decoder relies mainly on global self-attention and makes insufficient use of fine-grained structural priors such as local textures and edges.

MAEM equips each expert with a masked autoencoder (MAE), and a test node is assigned to the expert with the lowest reconstruction error; this decentralized routing mechanism requires no task ID and no shared parameters.

At inference time, MAE requires one forward pass per expert, and an increase in the number of experts linearly increases inference overhead.

MAE is a commonly used evaluation metric in forecasting tasks, calculated as the average of the absolute differences between predicted values and true values, with units consistent with electricity price, and the lower the value the better.

MAE (Masked Autoencoders) replaces the bottleneck constraint with a high masking ratio to force the model to learn semantic representations, continuing the design philosophy of Context Encoders.

This plug-and-play framework does not alter the structure or loss function of the SSL backbone network, but only replaces the sampling module, and can be integrated into existing SSL pipelines such as MAE, SimCLR, and DINO.

When the quantile level τ=0.5, quantile loss degenerates to Mean Absolute Error (MAE).

In recommendation scenarios, models typically use Root Mean Square Error (RMSE) to measure the error between predicted ratings and actual ratings.

Compared to the historical mean baseline, entry-level and turn-level predictions reduced mean absolute error (MAE) by 4.03% and 3.92%, respectively.

Geospatial foundation models typically use the Transformer architecture as their core, employing self-supervised learning methods for pre-training on large volumes of remote sensing imagery, with typical methods including masked autoencoders (MAE) and contrastive learning.

The MAE proposed by He et al. in 2021 splits images into patches, randomly masks up to 75% of the patches, encodes the visible patches with a Vision Transformer, and reconstructs the masked portions.