Tc,Tc/1.5} whereTcis the reference temperature (see Section “Methods” for details). The model is a quantum circuit with five qubits and an all-to-all qubit connectivity for the entangling layer
a, b, Average PCC (b) and CMD (c) values between the KNN-smoothing reference data and the predicted results for the intra-dataset scenario, that is, the training and test sets are from the same datasets. The X and Y axes are the cell‒cell and peak-peak PCC/CMD, respectively, and...
Developing a deep-learning-based helmet detection model usually requires an enormous amount of training data. However, there are very few public safety helmet datasets available in the literature, in which most of them are not entirely labeled, and the labeled one contains fewer classes. This ...
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 inTable 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:Evaluating...
A crucial step in the training process is the generation of the training datasets which provide a representative sampling of the multidimensional space of interest. For most of the ML regression methods, the computational effort needed to achieve a certain accuracy strongly depends on the size of ...
(2019) propose new scenarios that tackle the presentation of new training patterns of both known and unknown classes (New Instances and Classes - NIC), which include real-world challenges identified in some applications that were not considered before in continual learning scenarios. A recent trend...
Lables are in settings/setting_1/labels (labels adjusted to single class instead of 16 classes) Run adjust_labels.py to convert all annotation files to single class (class='0') Follow the instructions here for modifying the YOLOv7 repo for the finetuning experiments: article link to modif...
The aim of the generalizability study was to compare the performance of the methods using six training configurations with different composition and extent: three individual per-dataset training approaches (producing an individual model for each dataset) exploiting gold truth; silver truth; and both gol...
Our work addresses this gap by providing the first benchmark for three classes of synthetic clone models, namely supervised, self-supervised, and multi-modal ones, across a range of robustness measures. We show that existing synthetic self-supervised and multi-modal clones are comparable to or ...