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Found 4 papers, 1 papers with code
Continual Learning with Low Rank Adaptation
Recent work using pretrained transformers has shown impressive performance when fine-tuned with data from the downstream problem of interest.
PAUMER: Patch Pausing Transformer for Semantic Segmentation
We study the problem of improving the efficiency of segmentation transformers by using disparate amounts of computation for different parts of the image.
Test time Adaptation through Perturbation Robustness
Data samples generated by several real world processes are dynamic in nature \textit{i. e.}, their characteristics vary with time.
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration.
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