434,169 research outputs found
Applying SoftTriple Loss for Supervised Language Model Fine Tuning
We introduce a new loss function TripleEntropy, to improve classification
performance for fine-tuning general knowledge pre-trained language models based
on cross-entropy and SoftTriple loss. This loss function can improve the robust
RoBERTa baseline model fine-tuned with cross-entropy loss by about (0.02% -
2.29%). Thorough tests on popular datasets indicate a steady gain. The fewer
samples in the training dataset, the higher gain -- thus, for small-sized
dataset it is 0.78%, for medium-sized -- 0.86% for large -- 0.20% and for
extra-large 0.04%
Searching for Robustness: Loss Learning for Noisy Classification Tasks
We present a "learning to learn" approach for automatically constructing
white-box classification loss functions that are robust to label noise in the
training data. We parameterize a flexible family of loss functions using Taylor
polynomials, and apply evolutionary strategies to search for noise-robust
losses in this space. To learn re-usable loss functions that can apply to new
tasks, our fitness function scores their performance in aggregate across a
range of training dataset and architecture combinations. The resulting
white-box loss provides a simple and fast "plug-and-play" module that enables
effective noise-robust learning in diverse downstream tasks, without requiring
a special training procedure or network architecture. The efficacy of our
method is demonstrated on a variety of datasets with both synthetic and real
label noise, where we compare favourably to previous work
Deep learning based Machinery Threat Detection on Pipeline Right of Way
In this research, we develop a new deep learning strategy for robust detection and classification of objects on the pipeline right of way from aerial images. Our method can detect machinery threat with multiple sizes, different orientation and complex background in aerial images. In the proposed framework, the skip connection is used in the CNN structure to enhance feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network layer and the classifier layer.https://ecommons.udayton.edu/stander_posters/2701/thumbnail.jp
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning
In the domain of machine learning algorithms, the significance of the loss
function is paramount, especially in supervised learning tasks. It serves as a
fundamental pillar that profoundly influences the behavior and efficacy of
supervised learning algorithms. Traditional loss functions, while widely used,
often struggle to handle noisy and high-dimensional data, impede model
interpretability, and lead to slow convergence during training. In this paper,
we address the aforementioned constraints by proposing a novel robust, bounded,
sparse, and smooth (RoBoSS) loss function for supervised learning. Further, we
incorporate the RoBoSS loss function within the framework of support vector
machine (SVM) and introduce a new robust algorithm named
-SVM. For the theoretical analysis, the
classification-calibrated property and generalization ability are also
presented. These investigations are crucial for gaining deeper insights into
the performance of the RoBoSS loss function in the classification tasks and its
potential to generalize well to unseen data. To empirically demonstrate the
effectiveness of the proposed -SVM, we evaluate it on
real-world UCI and KEEL datasets from diverse domains. Additionally, to
exemplify the effectiveness of the proposed -SVM within the
biomedical realm, we evaluated it on two medical datasets: the
electroencephalogram (EEG) signal dataset and the breast cancer (BreaKHis)
dataset. The numerical results substantiate the superiority of the proposed
-SVM model, both in terms of its remarkable generalization
performance and its efficiency in training time
Contrastive Classification and Representation Learning with Probabilistic Interpretation
Cross entropy loss has served as the main objective function for
classification-based tasks. Widely deployed for learning neural network
classifiers, it shows both effectiveness and a probabilistic interpretation.
Recently, after the success of self supervised contrastive representation
learning methods, supervised contrastive methods have been proposed to learn
representations and have shown superior and more robust performance, compared
to solely training with cross entropy loss. However, cross entropy loss is
still needed to train the final classification layer. In this work, we
investigate the possibility of learning both the representation and the
classifier using one objective function that combines the robustness of
contrastive learning and the probabilistic interpretation of cross entropy
loss. First, we revisit a previously proposed contrastive-based objective
function that approximates cross entropy loss and present a simple extension to
learn the classifier jointly. Second, we propose a new version of the
supervised contrastive training that learns jointly the parameters of the
classifier and the backbone of the network. We empirically show that our
proposed objective functions show a significant improvement over the standard
cross entropy loss with more training stability and robustness in various
challenging settings
Rectified softmax loss with all-sided cost sensitivity for age estimation
In Convolutional Neural Network (ConvNet) based age estimation algorithms, softmax loss is usually chosen as the loss function directly, and the problems of Cost Sensitivity (CS), such as class imbalance and misclassification cost difference between different classes, are not considered. Focus on these problems, this paper constructs a rectified softmax loss function with all-sided CS, and proposes a novel cost-sensitive ConvNet based age estimation algorithm. Firstly, a loss function is established for each age category to solve the imbalance of the number of training samples. Then, a cost matrix is defined to reflect the cost difference caused by misclassification between different classes, thus constructing a new cost-sensitive error function. Finally, the above methods are merged to construct a rectified softmax loss function for ConvNet model, and a corresponding Back Propagation (BP) training scheme is designed to enable ConvNet network to learn robust face representation for age estimation during the training phase. Simultaneously, the rectified softmax loss is theoretically proved that it satisfies the general conditions of the loss function used for classification. The effectiveness of the proposed method is verified by experiments on face image datasets of different races. © 2013 IEEE
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