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Found 7 papers, 4 papers with code
FLARE up your data: Diffusion-based Augmentation Method in Astronomical Imaging
The intersection of Astronomy and AI encounters significant challenges related to issues such as noisy backgrounds, lower resolution (LR), and the intricate process of filtering and archiving images from advanced telescopes like the James Webb.
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance
The Fine-Tune Distillation framework aims to be adaptable to specific use cases, enabling the transfer of knowledge from the large models to the small models, making it suitable for domain-specific applications.
EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation
As virtual environments continue to advance, the demand for immersive and emotionally engaging experiences has grown.
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification
Deep Learning methods have recently seen increased adoption in medical imaging applications.
Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models
By leveraging a publicly available Brain MRI dataset, the experiment evaluated various transfer learning models for classifying different tumor types, including meningioma, glioma, and pituitary tumors.
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review
A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content.
On enhancing the robustness of Vision Transformers: Defensive Diffusion
Privacy and confidentiality of medical data are of utmost importance in healthcare settings.
