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Found 10 papers, 4 papers with code
UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition
The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation.
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification
We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation.
KutralNet: A Portable Deep Learning Model for Fire Recognition
The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops.
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy.
Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control
This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV).
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification
Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context.
Squeezed Deep 6DoF Object Detection Using Knowledge Distillation
We used the LINEMOD dataset to evaluate the proposed method, and the experimental results show that the proposed method reduces the memory requirement by almost 99\% in comparison to the original architecture with the cost of reducing half the accuracy in one of the metrics.
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum Entropy
Consequently, we propose IsoMax, a loss that is isotropic (distance-based) and produces high entropy (low confidence) posterior probability distributions despite still relying on cross-entropy minimization.
A writer-independent approach for offline signature verification using deep convolutional neural networks features
The use of features extracted using a deep convolutional neural network (CNN) combined with a writer-dependent (WD) SVM classifier resulted in significant improvement in performance of handwritten signature verification (HSV) when compared to the previous state-of-the-art methods.
Enhancing Batch Normalized Convolutional Networks using Displaced Rectifier Linear Units: A Systematic Comparative Study
Besides, statistical significant performance assessments (p<0. 05) showed DReLU enhanced the test accuracy presented by ReLU in all scenarios.
