When I am training deep learning model with different architectures, some time I have to change the batch size to prevent from Resource Exhausted Error condition. Is comparing the performance of these models trained in different batch size an issue or not?
I see lot research paper where batch size is defined fixed.
Batch size plays a crucial role in training deep learning models. While larger batches are conventionally believed to enhance model performance, there is evidence suggesting the contrary, especially when using autoencoders for data with global similarities and local differences. Large batch sizes can lead to a generalization gap, impacting model optimization and performance. For glaucoma detection, the optimal batch size for fine-tuned pre-trained models significantly influences performance, with ensemble models further enhancing results. In edge device applications, adjusting batch sizes can maximize GPU resource utilization and improve inference speed. Therefore, selecting an appropriate batch size is essential for optimizing deep learning model training and performance across various applications.
Smaller batch sizes surprisingly improve performance in medical autoencoders, capturing more biologically meaningful information and enhancing individual variation in latent spaces derived from EHR and medical imaging data.
Large batch sizes in deep learning models lead to increased near-rank loss of units' activation tensors, impacting model optimization and generalization.
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