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22 Apr 2022: Current mitigation techniques often rely onexpensive re-training procedures as new attacks emerge. ... In a real world deployment, earlyexit ensembles have the potential to have a high level ofrobustness provided by adversarial training of known

24 Aug 2022: due to its high accuracy and efficiency.This is akin to a scenario where a pretrained model (trainedwith a few shot learning method) can be used to identify new. ... Through a large number of experiments, we show that thesemethods can adapt to new

22 Apr 2022: model will be fine-tuned with new data received over time (and apart of the old data). ... So, the question is how to obtain a set ofweights that is more transferable to new classes.

4 Feb 2022: e., forward pass) [1], [2].To name a few, weight quantization [1], [3], pruning [2], vectorquantization [4], and new neural architectures [5], [6] havebeen proposed to make inference more efficient. ... Instead of finding a new dataset to meet our input

5 Sep 2022: domain. This implies that models are domain specific; for each new target domain, retraining is required. ... 2012. Introducing a New Benchmarked Dataset for Activity Monitoring. In 2012 16th International.

5 Sep 2022: It can also improve modelgeneralisation. This potentially paves a new pathway of utilis-ing unlabelled data effectively to build more accurate and reli-able COVID-19 detection tools.Index Terms:

5 Sep 2022: This leads to over-confidentwrong decisions when deploying in new environments, un-dermining the trust in these models. ... How to discover abrand new form or adapt existing forms for healthcare is a bigchallenge.

7 Mar 2022: Using the same mem-ory space between previous and new models, YONO can operatemultiple models within a limited memory budget of SRAM. ... Then, in 4.4, we select two new datasets in each of thefour modalities for a robust evaluation.