Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [transfer-learning]

For questions related to transfer learning, a machine learning method that focuses on storing knowledge gained while solving one problem in order to apply this knowledge to a different but related problem.

For more info, see https://en.wikipedia.org/wiki/Transfer_learning.

76 questions
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Is it possible to train a neural network as new classes are given?

I would like to train a neural network (NN) where the output classes are not (all) defined from the start. More and more classes will be introduced later based on incoming data. This means that, every time I introduce a new class, I would need to…
Fr_nkenstien
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1 answer

What is the difference between one-shot learning, transfer learning and fine tuning?

Lately, there are lots of posts on one-shot learning. I tried to figure out what it is by reading some articles. To me, it looks like similar to transfer learning, in which we can use pre-trained model weights to create our own model. Fine-tuning…
Hiren Namera
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What are the differences between transfer learning and meta learning?

What are the differences between meta-learning and transfer learning? I have read 2 articles on Quora and TowardDataScience. Meta learning is a part of machine learning theory in which some algorithms are applied on meta data about the case to…
Long
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7
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2 answers

Is there an argument against using the (reviewed) predictions of a model as ground truth to further train exactly this model?

I plan to use my predictions as ground truth to continue training my model. These predictions are of course reviewed during this process. Is there an argument against that (reinforcement of slight mistakes/overfitting etc.)? Here my specific use…
thzu
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6
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2 answers

What does "semantic gap" mean?

I was reading DT-LET: Deep transfer learning by exploring where to transfer, and it contains the following: It should be noted direct use of labeled source domain data on a new scene of target domain would result in poor performance due to the…
Kais Hasan
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6
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1 answer

What are the real-life applications of transfer learning?

What are the real-life applications of transfer learning in machine learning? I am particularly interested in industrial applications of the concept.
Ali Usyd
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6
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2 answers

What is the difference between learning without forgetting and transfer learning?

I would like to incrementally train my model with my current dataset and I asked this question on Github, which is what I'm using SSD MobileNet v1. Someone there told me about learning without forgetting. I'm now confused between learning without…
abhimanyuaryan
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5
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2 answers

How can I train a neural network for image classification when the dataset is small?

I need to train a convolutional neural network to classify snake images. The problem is that I have only a small number of images available for some snake types. So, what is the best approach to train a neural network for image classification using…
Lakwin Chandula
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4
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2 answers

Why does unsupervised pre-training help in deep learning?

What is the effectiveness of pre-training of unsupervised deep learning? Does unsupervised deep learning actually work?
kenorb
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4
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What is the relation between self-taught learning and transfer learning?

I am new to transfer learning and I start by reading A Survey on Transfer Learning, and it stated the following: according to different situations of labeled and unlabeled data in the source domain, we can further categorize the inductive transfer…
Kais Hasan
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4
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1 answer

How is few-shot learning different from transfer learning?

To my understanding, transfer learning helps to incorporate data from other related datasets and achieve the task with less labelled data (maybe in 100s of images per category). Few-shot learning seems to do the same, with maybe 5-20 images per…
Geneveve08
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Why aren't the BERT layers frozen during fine-tuning tasks?

During transfer learning in computer vision, I've seen that the layers of the base model are frozen if the images aren't too different from the model on which the base model is trained on. However, on the NLP side, I see that the layers of the BERT…
Bunny Rabbit
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4
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2 answers

What is layer freezing in transfer learning?

Transfer learning consists of taking features learned on one problem and leveraging them on a new, similar problem. In the Transfer Learning, we take layers from a previously trained model and freeze them. Why is this layer freezing required and…
Pluviophile
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How can I improve the performance of a model trained to detect vehicle poses?

I'm looking for some suggestions on how to improve our vehicle image recognition. We have an online marketplace where customers submit photos of their vehicles. The photos need to meet certain requirements before the advert can be approved.…
mechane
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When doing transfer learning, which initial layers do we need to freeze, and how should I change the last layer for my task?

I want to train a neural network for the detection of a single class, but I will be extending it to detect more classes. To solve this task, I selected the PyTorch framework. I came across transfer learning, where we fine-tune a pre-trained neural…
Santhosh
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