5

They are all related terms. From top to bottom: One-shot learning aims to achieve results with one or very few examples. Imagine an image classification task. You may show an apple and a knife to a human and no further examples are needed to continue classifying. That would be the ideal outcome, but for algorithms. In order to achieve one-shot learning (...


3

Yes, this is quite the expected behavior. The main difference between expected and current behavior lies in the amount of data you are using for training VS the amount of data that the pre-trained model was trained with. Take into account that pre-trained models have been trained over popular datasets, the most common ones are: COCO, ImageNet and Open Images....


1

This tells me that because the initial model was performing so poorly without any fine-tuning, most of the learning that led to the 90% accuracy was only because of the additional layers, not the layers that were transferred from the model trained on the D1 dataset. Is this a correct inference? This is a possibility, but not the only one. If you were re-...


1

The answer is yes but 'lightweight' will require a 'lightweight' model. Your application for 'domain one' is called open domain question answering (ODQA). Here is a demonstration of ODQA using BERT: https://www.pragnakalp.com/demos/BERT-NLP-QnA-Demo/ Your application for 'domain two' is a little different. It is about learning sequences from sequences. ...


1

Possibly a bit late to the answer, but I doubt you'd be able to run GPT-2 774M in FP32 on 2070 Super which has 8GB VRAM. I know it's not an exact comparison, but fine-tuning BERT Large (345M) in FP32 easily takes more than 10GB of VRAM. You might be able to run GPT-2 774M if you run it in FP16. Alternatively, you can use Google Collab TPUs which provide at ...


1

After a lot of browsing online for answers to these questions, this is what I came up with. What are the initial layers in this case? How exactly can I freeze them? The initial few layers are said to extract the most general features of any kind of image, like edges or corners of objects. So, I guess it actually would depend on the kind of backbone ...


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