Questions tagged [transfer-learning]

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model.predict() accuracy extremely low on training dataset

This question is similar to this. I'm new to ML, and I am trying to classify breast cancer histology images using EfficientNets with Transfer Learning. The dataset is small (400 images in total - ...
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What's the difference between transfer learning and feature extraction in CNN?

So from what i understand, transfer learning is the fact of training a model on a dataset where you have a lot of data, then keeping most of trained coefficients, and only re-training the last layer ...
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Transfer learning with many small datasets

Context I am working on a NLP-model that can classify documents into one of N categories. I have document data from a number of different customers. The document topics are similar across customers ...
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34 views

Why does Transfer Learning works better on smaller datasets than on larger ones?

This question is not about the utility of Tranfer Learning compared with regular supervised learning. 1. Context I'm studying Health-Monitoring techniques, and I practice on the C-MAPSS dataset. The ...
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169 views

Overfitting while fine-tuning pre-trained transformer

Pretrained transformers (GPT2, Bert, XLNET) is popular and useful because of their transfer learning capabilities. Just to remind: The goal of Transfer learning is is to transfer knowledge gained from ...
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181 views

Is Flatten() layer in keras necessary?

In CNN transfer learning, after applying convolution and pooling,is Flatten() layer necessary? I have seen an example where after removing top layer of a vgg16 ,first applied layer was ...
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Trouble with loading pre-trained CNN weights without classification layers for different input dimension

I am trying to load pre-trained CNN weights but without classification (i.e. top) layers. Basically, I want to do exactly what tf.keras.applications.ResNet50 class ...
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199 views

Why are results without Transfer Learning better than with Transfer Learning?

I developed a neural network for license plate recognition and used the EfficientNet architecture (https://keras.io/api/applications/efficientnet/#efficientnetb0-function) with and without pretrained ...
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22 views

How to combine the features extracted from different CNN architectures? [closed]

I want to combine the features extracted from different CNN architectures into my fully connected layer. How to proceed?
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63 views

Understanding Transfer Learning of Word Embeddings

I can't quite visualize how transfer learning of pre-trained word embeddings is useful in an NLP task( say named entity recognition ) . I'm studying Andrew NG's Sequence Models course and he seems to ...
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87 views

Imbalanced Dataset (Transformers): How to Decide on Class Weights?

I'm using SimpleTranformers to train and evaluate a model. Since the dataset I am using is severely imbalanced, it is recommended that I assign weights to each ...
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39 views

Any useful tips on transfer learning for a text classification task

I am doing a supervised binary text classification task. I want to classify the texts from site A, site B, and site C. The in-domain performance looks OK for texts of each site. (92%-94% accuracy). ...
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48 views

weight decay in ResNet50

Can someone please guide for implementing weight decay in transfer learning approach? I want to regularize the pre-trained model ResNet50, where I'm fine-tuning the model for an image classification ...
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Pre-trained CNN model makes Poor Predictions on Test Images Dataset

I have tried using several a pretrained models (MobileNet) for multiclass predictions. There are 42 classes and the distributions of the images are even across the 42 classes. This is my code: ...
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Transfer learning by using vgg in pytorch

I am using vgg16 for image classification. I want to test my transfered model with the following code: ...
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63 views

How many layers should I replace in transfer learning CNN

I am designing a convolutional neural network that I believe requires transfer learning to function in practice. The network will be a character level CNN for text classification, more specifically, ...
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25 views

ELMo - How does the model transfer its learning/weights on new sentences

Word2vec and Glove embeddings have the same vector representation for every word in the corpus and does not take context into consideration. For eg: The dog does bark at people The bark of the tree ...
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21 views

Transfer learning with Keras for medical image classification

Good afternoon; I'm trying to do Transfer Learning from pre-trained model on imagenet to solve a classification task with Lung CT slices. These slices are stored in ...
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32 views

What is the theoretical differences of Multitask learning vs Fine tuning based transfer learning?

Suppose, I have the following scenarios. I have a bunch of fruits, i.e., apple, orange, and banana. I simply made a Multitask model, where my network first tell me which fruit it is, and then telling ...
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Why siamese network using ResNet50 architecture has worse results than network trained from beginning?

I am trying to build product recognition tool based on ResNet50 architecture as below ...
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1answer
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Daily new data for my neural network, and I want transfer(?) learning

I made my neural network, it is pre-trained for 180 days of data. ...
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186 views

How to use fine tuning of BERT when i have unlabelled dataset of text documents?

I have gained a basic understanding of using BERT for various NLP/text mining tasks. When it comes to fine-tuning of BERT, I always see that fine-tuning is performed using some classification tasks. ...
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Transfer learning with different number of features

I have a newly collected dataset with 18 features, but since the study is just started, there only over 200 samples in this dataset. And for an old version of this study, we have over 8000 samples but ...
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1answer
64 views

Existing pre-trained NLP models to detect if a text input is a question

I would like to quickly filter text data into question and non-questions. Using the presence of question mark in the text is too crude. Are there any existing models I can use to aid me with my task?
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From Patch-based Classifier to Full Image classifier

I was wondering if it is feasible to train patch-based image classifier, due to small amount of data, and then use it in order to initialize training for full image classification, but this time on ...
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Are neural networks modular? An example

BACKGROUND Consider a supervised problem which is based on two scalar features (1) and (2) as well as a third, "time-dependent", feature consisting of a sequence of five values (3)-(7). For ...
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Can CNN do better than Transfer Learning?

With all my knowledge, I know that Transfer Learning do way better than CNN. I have a dataset of 855 images. I have applied CNN and got 94% accuracy.Then I applied Transfer Learning (VGG16, ResNet50, ...
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48 views

Using a custom test dataset on ImageDataBunch.from_name_re

I want to use the following piece of code: data = ImageDataBunch.from_name_re(image_path, food_names, file_parse, ds_tfms=get_transforms(), size=224) However, I ...
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Transfer Learning and Unknown Object Identification

Is it a good idea to use pre-trained weights of say VGG19 (or other) model for a classification problem for a completely a new class of object altogether? Whole idea is, I'm working on a binary image ...
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51 views

Multi-Class CNN model predicting only one class but still the accuracy is high

Before marking the question as repeated, note that I have read most of them but did not find the solution. I have given all the information on model below so please give advice for it. I am working ...
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Recursive Transfer Learning

Is there any methodology called Recursive Transfer Learning? For example, let's consider a situation that we have a lack of data while training a convolution neural network (CNN) for object detection ...
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What's the definition of retrainingļ¼Ÿ

In transfer learning, we always use new data to retrain the pre-trained model. But, what is the specific and official definition of retraining? Or what papers mentioned this definition, in transfer ...
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1answer
259 views

Error when trying Transfer Learning

I'm trying to train a model which is an extension of Google's Inception-V3 for the purpose of recognizing and classifying whether there is any pneumonia using x-ray images. I've used Tensorflow-Hub ...
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28 views

Real-life applications/examples of transfer learning approaches

I recently read a nice, informative paper titled 'A Survey on Transfer Learning'. It mentions 3 settings of transfer learning - inductive, transductive, and unsupervised. At the same time, it states ...
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How to load a saved model in TensorFlow?

This is my code in Python: ...
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27 views

Intensity image to RGB for transfert learning

My goal is to use a pre-trained model with intensity based image. Most pre-trained model expect RGB (int) format as input image. An easy workaround is to dupplicate the intensity channel 3 times in ...
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How to make sure that the learned weights are initialized instead of only layer structure?

I have trained a model for 100 epochs. The network is designed to save checkpoints after every 10th epoch. Besides this, once the training finished I saved the model using these commands: ...
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Transfer learning from saved frozen graph

Using tensorflow graph (core tensor api's), I have trained a model and saved as a frozen graph. As a part of transfer learning, I want to use these trained layers and extend my ML network with some ...
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What is a good pre-trained model to start transfer learning with medical images (CT scans, histopathology, mammography etc)

I am working on multiple medical imaging datasets for image classification problems. ResNet (18 or 34) is something I start with but since there are so many options out there, I curious if there are ...
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83 views

Negative examples for a Yes/No image classification neural network

I'm trying to retrain a neural network using transfer learning that can classify whether an image has a certain object, say, a car. My positive sample dataset is quite small, only 2500~ images. It ...
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Transfer Learning Question: Extending the Functionality of a Multipose-Estimation Machine Learning Model?

I have experimented with a number of different machine learning models used for pose estimation. Most of them output a heatmap and offsets for the detected person(s) in the image. I really like the ...
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263 views

How to use MNIST dataset to make predictions on similar images (colorblindness charts)?

I am trying to use the MNIST dataset to train a convolutional neural network to classify digits written in colorblindness charts. As some people have suggested, I have tried playing with the ...
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1answer
48 views

How to choose layer from which to unfreeze image classification model

I'm wondering what steps do you take to decide on the part of the model to unfreeze. Do you do multiple experiments? Since the use of GPU is expensive, you must have some guidelines. Note: I know ...
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243 views

PyTorch: How to use pytorch pretrained for single channel image

If I have to create a model in pytorch for images having only single channel. How can I transform my model to adopt to this new architecture without having the need to compromise the pre-trained ...
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103 views

Why is convnet transfer learning taking so long?

I am using transfer learning to train a binary image classification model using keras' pretrained VGG16 model. The code can be found below : ...
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Using Orange3 to embed the image, the raw data need to upload to a remote server?

the official document talk about : Image Embedding reads images and uploads them to a remote server or evaluate them locally. Deep learning models are used to calculate a feature vector for each image....
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Object re identification through two cameras

I've been recently using the YOLO to detect trucks in images, which turned out really well. My next step is to try to find images of the same truck across the whole set of images I've retrieved ...
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119 views

Comparison with transfer learning and CNN from scratch on a small dataset

I am doing image classification with CNN, and I have a training set of 3200 imges and a training set of 400 images. I have used two different approches for doing this classification : Transfer ...
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Training of a CNN stops at the seventh epoch

I am doing image classification, and I am using transfer learning to do this. My problem is that if I build the network, and then I train it, the training process stops at the seventh epoch, even if I ...