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.

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Precise description of one-shot learning

I am working on classifying the Omniglot dataset and the different papers dealing with this topic describe the problem as one-shot learning (classification). I would like to nail down a precise ...
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1answer
68 views

What are the real-life applications of Transfer Learning in Machine Learning?

What are the real-life applications of Transfer Learning in Machine Learning? I am particularly interested in industrial applications of the concept.
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How to transfer learn Darknet YOLOv3

I've started getting into object detection in image. I have YOLOv3 neural network with Darknet framework. The network is pre-trained from COCO data set. Now I need to do some transfer learning in ...
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Training, validation loss and accuracy yolov3?

This is a version of Yolo V3 implemented in PyTorch – YOLOv3 in PyTorch I am trying to use transfer learning to train this yolov3 implementation following the directions given in this post. This is ...
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1answer
25 views

Transfer learning to train only for a new class while not affecting the predictions of the other class

I am basically interested in vehicle on the road. YoloV3 pytorch is giving a decent result. So my interested Vehicles Car ...
2
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1answer
72 views

How is transfer learning used to mitigate catastrophic forgetting in neural networks?

How can transfer learning be used to mitigate catastrophic forgetting. Could someone elaborate on this?
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1answer
51 views

What are the most common methods to enable neural networks to adapt to changing environments?

For real applications, concept drifts often exist, i.e., the relationship between the input and output changes overtime. Thus, we need our AI or machine learning system to quickly adapt to the ...
3
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2answers
81 views

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 issues, which is what I'm using SSD MobileNet v1: https://github.com/tensorflow/models/issues/...
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27 views

Is convergence to a local minima more likely with transfer learning?

While doing transfer learning where my two problems are face-generation and car-generation is it likely that, if I use the weights of one problem as the initialization of the weights for the other ...
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Paper & code for “unsupervised domain adaptation” for regression task

Does anyone know a paper or code that does "unsupervised domain adaptation" for regression task? I saw most of the papers were benchmarked on classification tasks, not regression. I want to do ...
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0answers
12 views

Binary annotations on large, heterogenous images

I'm working on a deep learning project and have encountered a problem. The images that I'm using are very large and extremely detailed. They also contain a huge amount of necessary visual information, ...
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1answer
41 views

Are there any better visual models for transfer rather than ImageNet?

Similar to the recent pushes in Pretrained Language Models (BERT, GPT2, XLNet) I was wondering if such a thrust exists in Computer Vision? From my understanding, it seems the community has converged ...
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Training a reinforcement learning model with multiple images

I am tentatively trying to train a deep reinforcement learning model the maze escaping task, and each time it takes one image as the input (e.g., a different "maze"). Suppose I have about $10K$ ...
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Is it possible to train a neural network incrementally?

I would like to train a neural network 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 ...