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This research investigates the generalization capabilities of neural networks in deep learning when applied to real-world scenarios where data often contains imperfections, focusing on their adaptability to both noisy and non-noisy scenarios for image retrieval tasks. Our study explores approaches to preserve all available data, regardless of quality, for diverse tasks. The evaluation of results varies per task, due to the ultimate goal of developing a technique to extract relevant information while disregarding noise in the final network design for each specific task. The aim is to enhance accessibility and efficiency of AI across diverse tasks, particularly for individuals or countries with limited resources, lacking access to high-quality data. The dedication is directed towards fostering inclusivity and unlocking the potential of AI for wide-spread societal benefit.