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Harnessing The Power of Attention For Patch-Based Biomedical Image Classification
Biomedical image analysis can be facilitated by an innovative architecture rooted in self-attention mechanisms.
Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification
Group Normalization computations are accurate across a wide range of batch sizes and are independent of batch size.
A Comprehensive Review of Knowledge Distillation in Computer Vision
Deep learning techniques have been demonstrated to surpass preceding cutting-edge machine learning techniques in recent years, with computer vision being one of the most prominent examples.
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression
With the rapid development of computer vision, Vision Transformers (ViTs) offer the tantalizing prospect of unified information processing across visual and textual domains.
Knowledge Distillation in Vision Transformers: A Critical Review
A number of model compression methods have been proposed in the literature such as weight quantization, weight multiplexing, pruning and Knowledge Distillation (KD).
