Transfer learning is invariably effective. eg. Irrespective of the amount of data, we can always rely on transfer learning.

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Question: Transfer learning is invariably effective. eg. Irrespective of the amount of data, we can always rely on transfer learning.

Option 1: True. This statement claims that transfer learning is always effective regardless of data quantity or context. In reality, transfer learning can be powerful when the source and target tasks are related and the target data, while limited, is representative of the target domain. However, there are several caveats: if the source and target domains are very different (domain shift), or if the target task requires unique features not present in the pre-trained model, transfer learning may underperform or even hurt performance. Additionally, when the target dataset is extremely small or noisily labeled, fine-tuning can lead to overfitting or poor generalization. Therefore, the blanket claim that transfer learning is invariably effective across all scenarios is incorrect.

Option 2: False. This option states that transfer learning is not invariably effective in all cases. This aligns with common understanding in machine learning: effectiveness depends on task relatedness, data distribution similarity, model capacity, and the amount and quality of target data. If the source model is poorly aligned with the target task, or if there is substantial domain mismatch, or if the target data is abundant and the best approach is to train from scratch, transfer learning may offer little benefit. Moreover, improper fine-tuning, negative transfer, or architectural incompatibilities can degrade performance compared to training a model from scratch or using alternative methods. Thus, saying transfer learning is not invariably effective correctly captures the nuanced reality of when and how it helps.

In summary, the reasoning for each option reflects a nuanced view: while transfer learning is a powerful tool in many situations, its success is not guaranteed in every scenario due to domain similarity, data quality, and task requirements, among other factors. The statement in Option 1 overgeneralizes this and is therefore incorrect, while Option 2 accurately communicates the caveats about transfer learning’s effectiveness across diverse settings.

CS-7643-O01, OAN, OSZ Quiz #3: Convolutional Neural Network Architectures (Lesson 6), Visualization (Lesson 7), Advanced Computer Vision Architectures (Lesson 8)

Transfer learning is invariably effective. eg. Irrespective of the amount of data, we can always rely on transfer learning.

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