Search Results For Autodata (2) ((FULL))
Summary and Contributions: This paper tackles automated data augmentation by first identifying the problem of policy search on proxy task, a standard practice in the field. It then propose a reduced search space to find good data augmentation strategies. The resultant search space is simple enough for grid search yet still yield competitive performance compared to other automated data augmentation methods.
Search results for autodata (2)
Summary and Contributions: This paper proposes an efficient method to search for data augmentation policies. It conducts a simple grid search on a vastly reduced search space. The experimental results show the competitive performances with some previous work on multiple tasks.
Strengths: 1. This work empirically studies the relationship between the optimal strength of data augmentation and the model size/training set size. The observation indicates that proxy-task-based search methods may be sub-optimal for learning and transferring augmentation policies.2. This work designs a small policy search space. It significantly reduces the computation cost, getting rid of a separate expensive search phase and proxy tasks.3. Experimental results are shown for CIFAR-10/100, SVHN, ImageNet, and COCO datasets. This method achieves equal or better performance over some previous methods.
Weaknesses: The mothod of this paper are not so complecate. The author made the point clear that their work decovers the power of the random augmentation. However, some how I think some conventional augmentation procedure doesn't seems entirely another category. Personaly, I would like to see more inovention in this work.2. This work uses a much smaller search space than much previous work, resulting in fixed searched policies. However, PBA [2] points out that an augmentation function that can reduce generalization error at the end of training is not necessarily a good function at initial phases. So the end goal of PBA is to learn a schedule of augmentation policies as opposed to a fixed policy. Besides, previous works [3,4] also replaces the fixed augmentation policy with a dynamic schedule of augmentation policy along with the training process, and achieves better performance than this paper with competitive computational costs. All of these may indicate the limitations of the fixed policies.Reference:[1] Cubuk, Ekin D., et al. "Autoaugment: Learning augmentation strategies from data." Proceedings of the IEEE conference on computer vision and pattern recognition. 2019.[2] Ho, Daniel, et al. "Population based augmentation: Efficient learning of augmentation policy schedules." International Conference on Machine Learning. 2019.[3] Zhang, Xinyu, et al. "Adversarial autoaugment."International Conference on Learning Representations . 2020.[4] Lin, Chen, et al."Online hyper-parameter learning for auto-augmentation strategy" Proceedings of the IEEE International Conference on Computer Vision. 2019.
Summary and Contributions: The author proposed a novel automated data augmentation method named "RandAugment". It designed a vastly smaller search space than the previous works, thus reducing the computation expense. Moreover, in the proposed search space, the author used a random search strategy that achieved some promising results on classification and object detection tasks in automated data augmentation. I have read the rebuttal and would keep the score.
Strengths: 1. Comparing to previous works, the proposed method could reduce the search space from 10^32 to only 100. Moreover, the proposed work is easy to implement thanks to the random search or grid search. 2. The ablation experiments seems ok and adequately designed. It's interesting that posterize has a negative impact on these datasets and rotate\shear\translate are the most effective transformation which is consistent with common sense.3. The experiments verify that the proxy task may provide sub-optimal results and the proposed method could be directly used on large datasets. Furthermore, RandAugment is largely insensitive to the selection of transformations for different datasets. 4. Paper is well written and easy to understand.
Weaknesses: 1. Although experimental results show the effect of the proposed method, the contribution of this work may be incremental. The idea of this paper seems to come from the paper "EVALUATING THE SEARCH PHASE OF NEURAL ARCHITECTURE SEARCH" that random search performs better than elaborate search strategies. Could you highlight your main contributions and the differerence with it, as well as why such differences make sense?2. It's encouraged to give the details of the search space of previous works like AA, Fast AA, and PBA.3. It would be better to place the time cost in the other tables of experiments like in Table 1.
Summary and Contributions: This work proposes a simple and small space to reduce the cost of augmentation search. It samples N transformation operations from the pool, and set a scale M of distortion parameters for all sampled operations. This method leads to significant improvement on multiple tasks like image classification and object detection at minor cost.
Strengths: The paper proposes an extremely simple search space, yet the empirical improvements are significant. It conducts comprehensive empirical experiments with well described training details.
Weaknesses: - Although the empirical study shows good results, the novalty of this paper is limited.- The paper claims a simple grid search is sufficient to get a good result. However a random search might give better result at the same cost, because the optimal parameter may not be included in the grid.- The advantage of this method is obvious when the pool of all transforms is large. Still it is unclear to see whether it is still outstanding when the number of transformation is small. Not all scenarios have a list of 14 augmentations, thus such ablation study is important.
Additional Feedback: - Conduct experiment with random search instead of the grid search, and compare the results. Faster convergence and more robust performance is expected from random search given a large pool of augmentations.- Conduct experiment with a smaller pool of transformation candidates and compare with the baselines. It is possible that other augmentation searching algorithms may be comparable or even superior since they might have better results in a reasonable amout of time given a small pool to search from.Update after Rebuttal:I've read the feedback and other reviews. The feedback on novelty is not persuasive enough to me. The statement "...many new semi-supervised and self-supervised papers utilize this method to achieve SOTA..." is more on the side of useful-ness but not the novelty. On the other hand, it is still clear to see that the method brings improvement. Therefore, I'd like to keep my score.
Dimensions allow aggregation of results of multiple data quality rules formonitoring and alerting. Every rule in Dataplex AutoDQ must beassociated with a dimension. Dataplex supports the followingdimensions:
Dataplex data quality Public Preview is currently free. Billingwill be enabled sometime during the Public Preview phase with at least amonth of notice in advance. In Public Preview, publishing data quality resultsto Data Catalog is not currently available. When it becomesavailable, it will be charged with Data Catalog metadata storagepricing. See Pricing for more details.
In the above test case I would like AutoFixture to generate only the last parameter: "double third" and use data available in TestSpecificData theory data for the first two parameters.Attempting to run above code results in InvalidOperationException with following message:
Power BI enables you to go from data to insight to action quickly, yet you must make sure the data in your Power BI reports and dashboards is recent. Knowing how to refresh the data is often critical in delivering accurate results.
Power BI imports the data from the original data sources into the dataset. Power BI report and dashboard queries submitted to the dataset return results from the imported tables and columns. You might consider such a dataset a point-in-time copy. Because Power BI copies the data, you must refresh the dataset to fetch changes from the underlying data sources.
Power BI doesn't import data over connections that operate in DirectQuery mode. Instead, the dataset returns results from the underlying data source whenever a report or dashboard queries the dataset. Power BI transforms and forwards the queries to the data source.
Because Power BI doesn't import the data, you don't need to run a data refresh. However, Power BI still performs tile refreshes and possibly report refreshes, as the next section on refresh types explains. A tile is a report visual pinned to a dashboard, and dashboard tile refreshes happen about every hour so that the tiles show recent results. You can change the schedule in the dataset settings, as in the screenshot below, or force a dashboard update manually by using the Refresh now option.
If your dataset resides on a Premium capacity, you might be able to improve the performance of any associated reports and dashboards by enabling query caching, as in the following screenshot. Query caching instructs the Premium capacity to use its local caching service to maintain query results, avoiding having the underlying data source compute those results. For more information, see Query caching in Power BI Premium.
Following a data refresh, however, previously cached query results are no longer valid. Power BI discards these cached results and must rebuild them. For this reason, query caching might not be as beneficial for reports and dashboards associated with datasets that you refresh often, for example 48 times per day.
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