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Found 5 papers, 2 papers with code
Energy and Carbon Considerations of Fine-Tuning BERT
Despite the popularity of the `pre-train then fine-tune' paradigm in the NLP community, existing work quantifying energy costs and associated carbon emissions has largely focused on language model pre-training.
One Hyper-Initializer for All Network Architectures in Medical Image Analysis
Pre-training is essential to deep learning model performance, especially in medical image analysis tasks where limited training data are available.
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification
Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world data differ in imaging equipment manufacturer, image acquisition protocol, patient populations, etc.
An Effective Transformer-based Solution for RSNA Intracranial Hemorrhage Detection Competition
Nearly all the top solutions rely on 2D convolutional networks and sequential models (Bidirectional GRU or LSTM) to extract intra-slice and inter-slice features, respectively.
Progressive Hard-case Mining across Pyramid Levels for Object Detection
Based on focal loss with ATSS-R50, our approach achieves 40. 5 AP, surpassing the state-of-the-art QFL (Quality Focal Loss, 39. 9 AP) and VFL (Varifocal Loss, 40. 1 AP).
