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Found 6 papers, 4 papers with code
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.
