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Found 34 papers, 9 papers with code
Generative Dataset Distillation Based on Self-knowledge Distillation
Dataset distillation is an effective technique for reducing the cost and complexity of model training while maintaining performance by compressing large datasets into smaller, more efficient versions.
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images
We propose a novel continual self-supervised learning method (CSSL) considering medical domain knowledge in chest CT images.
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence
The Matryoshka Mamba and the hybrid contrastive learning paradigm operate in two parallel branches within Manta, enhancing Mamba for FSAR of long sub-sequence.
Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume
Therefore, in the field of medical imaging, there remains a lack of simple and practical denoising methods that can achieve high-quality denoising performance using only single noisy images.
Think Twice Before Recognizing: Large Multimodal Models for General Fine-grained Traffic Sign Recognition
The differential descriptions of similar traffic signs optimize the multimodal thinking capability of the LMM.
Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Data
This work aims to develop a novel medical image-to-image translation model that is independent of pixel-wise aligned data (MITIA), enabling reliable multi-modal medical image-to-image translation under the condition of misaligned training data.
Generative Dataset Distillation Based on Diffusion Model
In this study, we proposed a novel generative dataset distillation method based on Stable Diffusion.
Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition
To reduce the dependence on training data and improve the performance stability of cross-country TSR, we introduce a cross-domain few-shot in-context learning method based on the MLLM.
SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics
Model Predictive Control (MPC)-based trajectory planning has been widely used in robotics, and incorporating Control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency.
Generative Dataset Distillation: Balancing Global Structure and Local Details
In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model.
Importance-Aware Adaptive Dataset Distillation
Based on this observation, we propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance by automatically assigning importance weights to different network parameters during distillation, thereby synthesizing more robust distilled datasets.
PainSeeker: An Automated Method for Assessing Pain in Rats Through Facial Expressions
In this letter, we aim to investigate whether laboratory rats' pain can be automatically assessed through their facial expressions.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling
On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance.
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images
We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method.
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
Our method adopts a new masking strategy that utilizes organ mask information to identify valid regions for learning more meaningful representations.
Dataset Distillation Using Parameter Pruning
In this study, we propose a novel dataset distillation method based on parameter pruning.
Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum
Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be used for classifier selection and dataset reduction.
Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing
Furthermore, our method can extract essential weights of DCNN models to reduce the memory required to save trained models for efficient medical data sharing.
Dataset Distillation for Medical Dataset Sharing
Sharing medical datasets between hospitals is challenging because of the privacy-protection problem and the massive cost of transmitting and storing many high-resolution medical images.
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images
The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems.
TriBYOL: Triplet BYOL for Self-Supervised Representation Learning
Our method provides a feasible solution for self-supervised learning with real-world high-resolution images that uses small batch sizes.
Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis
The results show that DRCA improved the classification accuracy on six subjects (p < 0. 05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement.
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose
As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN).
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.
Soft-Label Anonymous Gastric X-ray Image Distillation
The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution.
Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images
The effectiveness of the proposed self-supervised learning method in gastritis detection is verified using a few annotated gastric X-ray images.
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
Furthermore, this study provides a systematic analysis of different augmentation strategies.
A review of smartphones based indoor positioning: challenges and applications
The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
Entangled Transformer for Image Captioning
This phenomenon is known as the semantic gap between vision and language.
Tool Breakage Detection using Deep Learning
In this work, we use the spindle current approach to detect the breakage of machine tools, which has the high performance of real-time monitoring, low cost, and easy to install.
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.
