classes of serious segmentation errors altogether. This paper introduces a vectorial score that is sensitive to, and identifies, the most important classes of segmentation errors (over-, under-, and mis-segmentation) and what page components (lines, blocks, etc.) are affected. Unlike previous ...
The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised ...
External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone ...
For offline learning (i.e., without incremental training), set--base_classesto the total number of classes in the dataset and--new_classes = 0. For experiments with permuted datasets, set--base_classes = --new_classeswhere--base_classes= the total number of classes in the dataset. ...
Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse ...
External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone ...
The learning-based dehazing methods usually require large amounts of paired training data for training and evaluation. Up till now, several datasets for image dehazing have been proposed. We list the widely adopted datasets in Table 3. According to the ways of data acquirement, the datasets can...
Benchmarking Storage Classes:Testing cloud block storage, ZFS with compression, and local storage for database workloads. ZFS Performance Advantages:Exploring how ZFS compression reduces disk usage while improving transaction rates. Trade-offs in Storage Solutions:Evaluatin...
We also follow OpenOOD to manage the training and testing datasets. If you only use our evaluator, the benchmarks for evaluation will be automatically downloaded by the evaluator (again check out this tutorial). If you would like to also use OpenOOD-VLM for training, you can get all data...
This section is responsible for generating and labelling the training data used to train the Machine Learning models. It has several options to change the generation algorithm and vary hyperparameters to that algorithm, as well as specifying number of dimensions and size of the maps. They are sav...