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Found 26 papers, 3 papers with code
Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency
AdaPruner can still significantly enhance model performance even after pruning up to 10-30\% of the training data.
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields
Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns.
RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition
RADAP employs innovative techniques, such as FCutout and F-patch, which use Fourier space sampling masks to improve the occlusion robustness of the FR model and the performance of the patch segmenter.
A Simple Geometric-Aware Indoor Positioning Interpolation Algorithm Based on Manifold Learning
Interpolation methodologies have been widely used within the domain of indoor positioning systems.
A Long-Tail Friendly Representation Framework for Artist and Music Similarity
The investigation of the similarity between artists and music is crucial in music retrieval and recommendation, and addressing the challenge of the long-tail phenomenon is increasingly important.
RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition
Data augmentation has been widely used in low-resource NER tasks to tackle the problem of data sparsity.
GIMM: InfoMin-Max for Automated Graph Contrastive Learning
Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessary for the downstream task.
Attribute-Specific Manipulation Based on Layer-Wise Channels
We analyse the gradients layer by layer on the style space.
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification
The experimental results on various datasets and CNN models verify that the proposed method outperforms other previous data augmentation methods in image classification tasks.
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance
In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models.
RSTAM: An Effective Black-Box Impersonation Attack on Face Recognition using a Mobile and Compact Printer
Furthermore, we propose a random meta-optimization strategy for ensembling several pre-trained face models to generate more general adversarial masks.
Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information
From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios.
RandoMix: A mixed sample data augmentation method with multiple mixed modes
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains.
Image Data Augmentation for Deep Learning: A Survey
By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data.
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack
A special branch of adversarial examples, namely sparse adversarial examples, can fool the target DNNs by perturbing only a few pixels.
Inf-CP: A Reliable Channel Pruning based on Channel Influence
We perform extensive experiments to prove that pruning based on the influence function using the idea of ensemble learning will be much more effective than just focusing on error reconstruction.
SASICM A Multi-Task Benchmark For Subtext Recognition
The F1 score of SASICMBERT, whose pretrained model is BERT, is 65. 12%, which is 0. 75% higher than that of SASICMg.
Faster and Simpler Siamese Network for Single Object Tracking
With the development of the deep network and the release for a series of large scale datasets for single object tracking, siamese networks have been proposed and perform better than most of the traditional methods.
IC Networks: Remodeling the Basic Unit for Convolutional Neural Networks
Inspired by the elastic collision model in physics, we present a general structure which can be integrated into the existing CNNs to improve their performance.
IC Neuron: An Efficient Unit to Construct Neural Networks
We believe that the IC neuron can be a basic unit to build network structures.
Temporal Convolutional Attention-based Network For Sequence Modeling
So we propose an exploratory architecture referred to Temporal Convolutional Attention-based Network (TCAN) which combines temporal convolutional network and attention mechanism.
IC-Network: Efficient Structure for Convolutional Neural Networks
We term this structure the "Inter-layer Collision" (IC) structure.
Pairwise Interactive Graph Attention Network for Context-Aware Recommendation
PIGAT introduces the attention mechanism to consider the importance of each interacted user/item to both the user and the item, which captures user interests, item attractions and their influence on the recommendation context.
Super Interaction Neural Network
Recent studies have demonstrated that the convolutional networks heavily rely on the quality and quantity of generated features.
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning
In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.
Operation-aware Neural Networks for User Response Prediction
Many deep models are proposed to automatically learn high-order feature interactions.
