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Found 5 papers, 4 papers with code
What Do Self-Supervised Vision Transformers Learn?
We present a comparative study on how and why contrastive learning (CL) and masked image modeling (MIM) differ in their representations and in their performance of downstream tasks.
How Do Vision Transformers Work?
In particular, we demonstrate the following properties of MSAs and Vision Transformers (ViTs): (1) MSAs improve not only accuracy but also generalization by flattening the loss landscapes.
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
Neural network ensembles, such as Bayesian neural networks (BNNs), have shown success in the areas of uncertainty estimation and robustness.
Differentiable Bayesian Neural Network Inference for Data Streams
Instead of generating separate prediction for each data sample independently, this model estimates the increments of prediction for a new data sample from the previous predictions.
Vector Quantized Bayesian Neural Network Inference for Data Streams
The computational cost of this model is almost the same as that of non-Bayesian NNs.
