Questions tagged [transfer-learning]

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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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40 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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164 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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1answer
17 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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1answer
23 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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2answers
43 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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1answer
35 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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31 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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1answer
21 views

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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13 views

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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2answers
38 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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1answer
19 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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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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1answer
31 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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14 views

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
20 views

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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1answer
50 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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16 views

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
35 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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28 views

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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28 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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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
185 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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20 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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731 views

How to load a saved model in TensorFlow?

This is my code in Python: ...
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1answer
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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1answer
64 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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1answer
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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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1answer
258 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
40 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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2answers
198 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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85 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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1answer
104 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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25 views

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 ...
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2answers
316 views

Model not learning when using transfer learning

I am working on a personal project on image classification (two classes) and am trying to see how the MobileNet v2 structure would perform. While training the training accuracy is already quite high ...
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4answers
27 views

Image Classification using Single Class Dataset using Transfer Learning [closed]

I only have around 1000 images of vehicle. I need to train a model that can identify if the image is vehicle or not-vehicle. I do not have a dataset for not-vehicle, as it could be anything besides ...
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1answer
32 views

Transfer learning between Language Model and classification

Following this fast.ai lecture, I am trying to understand the mechanism of Transfer Learning in NLP from a general Language Model (LM) to a classification problem. What is exactly taken from the ...
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1answer
138 views

Why/When should I use VGG16 to do fine-tuning? [closed]

Why or When should I use VGG16 in my cnn? what is the pros and cons to use this model? I search but not found this answer. If you have references, I appreciate