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Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework
Subsequently, a new dataset becomes available, prompting the desire to make a pivotal decision for achieving enhanced and leveraged inference performance on both sides: Should one opt to train datasets from scratch or fine-tune the model trained on the initial dataset using the newly released dataset?
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification
In this paper, we propose a No-Reference Image Quality Assessment (NRIQA) guided cut-off point selection (CPS) strategy to enhance the performance of a fine-grained classification system.
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