One common solution [18,44] is to adopt hard negative mining during training. Recently, several methods [6,34] re-weight the contribution of each sample based on the observed loss and demonstrate significant improvements on segmentation and detection tasks. In this work, we propose a cost-...
Notably, our method is different from the existing online hard example mining methods [10,8]. 例如,在线硬示例挖掘[10]和目标检测中的焦点损失[8]很难转移到弱监督场景,因为它们会放大伪掩模pseudo-mask中的噪声。For instance, online hard example mining [10] and focal loss [8] in object detection ...
Online triplet loss with negative mining TODO Optimize triplet selection Evaluate with a metric that is comparable between approaches Evaluate in one-shot setting when classes from test set are not in train set Show online triplet selection example on more difficult datasets ...
To address the issue of imbalance between positive and negative example, an Online Hard Example Mining (OHEM) method [24] is introduced. This approach focuses only on the Regions of Interest (ROIs) that are most beneficial for backpropagation. OHEM are ranked based on the loss from the forwar...
Online triplet loss with negative mining TODO Optimize triplet selection Evaluate with a metric that is comparable between approaches Evaluate in one-shot setting when classes from test set are not in train set Show online triplet selection example on more difficult datasets ...
The cross-entropy loss function was used as the loss function, and the AdamW method was employed for model training optimization. A five-fold cross-validation procedure was utilized to run our proposed model. During the training process, we performed fine-tuning on Roberta. The input dimension,...
As far as online password guessing is concerned, it is equally hard because it again needs insertion of two correct values {ID, PW} simultaneously which is not possible in real time polynomial. Thus, it seems that the scheme provides resistance to offline/online password guessing attacks. View...
50 layers for Bi-GRU with L2 regularization of 1e−8 factor, 100 layers for dense layer with an ReLU activation and L2 regularization of 1e−8 factor, 1 attention layer with normal distribution initialization, Adam optimizer of 0.001 learning rate, and categorical cross-entropy loss strategy...
such as cross-entropy for classification tasks, LT is the embedding loss for tree group T and defined in Eqn.(10), k is the number of tree groups, α and β are hyper-parameters given in advance and used for controlling the strength of end-to-end loss and embedding loss, respectively....
Free and/or open source books on machine learning, statistics, data mining, etc (https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md) Lucene in Action - Second Edition (https://livebook.manning.com/book/lucene-in-action-second-edition/appendix-b/) Build a Large Lan...