Questions tagged [convolutional-neural-networks]

For questions about convolutional neural networks, also known as CNN or ConvNet.

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Why is my convolutional neural network failing to classify user inputted images after having high accuracy in testing?

My CNN was trained on the Kaggle A-Z Dataset and consists of: ...
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Why do Convolution Neural Networks work on NLP/sequential tasks?

I have read some articles where people use 1D CNN for NLP tasks like sentiment analysis. My questions are, given that CNNs are largely used for images, how/why does this work on sequences/NLP tasks? ...
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If I am using a CNN trained with the triplet loss to perform speaker verification, what labels should I use for the training process?

If I am using a Convolutional Neural Network trained with the Triplet Loss (also combined with GAN and a classifier) for building a model that performs Speaker Verification, what labels should I use ...
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What architecture would be best to match images of torn pieces of tapes?

I am currently working on a project where the goal is to create a neural network that can determine if two pieces of torn tapes are a true fit or not. My current idea is a convolutional network that ...
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What are some of the commonly used image processing techniques of OpenCV for multiclass image classification?

I'm working on multiclass skin disease image classification(caused by bacteria and fungus). Some of the sample images are shown below. Images contain different background as shown in image_1 and ...
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What are some solid metrics to evaluate/compare the outputs of explainable algorithms?

Consider a learned CNN image classifier and a task that focuses on studying the outputs of explainable algorithms, such as integrated gradients and grad-cam, on the classifier's predictions. I am ...
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Denoise autoencoder not training properly [closed]

I'm trying to make a denoise autoencoder wherein the encoder part is vgg16 and decoder is opposite of vgg16(encoder) network. My dataset consists of 5K images in grayscale and these are the steps i've ...
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Finer control over the distribution of output from a GAN

I have cast some data into an image (25 x 56) to work better with existing tools, and then used a CNN to train a GAN using the Wasserstein loss with gradient penalty to generate new samples of the ...
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How can I reduce the loss? Why do I have the high loss and why do I have the gradient?

I want to classify some images (there are about 200.000 images) with a CNN. But I get a very high loss, see figures: Loss over the hole training run Loss for each epoch It's confused me, that there ...
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Is there a way to further fine tune image alignment homography after using Spatial Transformer Networks?

I'm using a Spatial Transformer Networks (STN) to align an image by optimization using L1 loss between prediction and target, they works great for large mismatch, but not so much for small ...
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What is the time complexity of Deep VGG-16 Net?

I would like to compute the time complexity (in Big O notation) of Deep Vgg16 Net by feeding forward 1000 images with size 224x224x3 and retrieving features from the second fully connected layer 'fc7' ...
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Quick Lightweight Image Segmentation Model For Training on Custom COCO-Format Dataset

I'm trying to build a model for image segmentation on a Raspberry Pi. I have a dataset with annotations in the COCO-format that took a long time to build, so I'd prefer not to have to build another ...
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Training a u-net for multi-landmark heatmap regression producing the same heatmap for each channel

I’m training a U-Net (model below) to predict 4 heatmaps (gaussian centered around a keypoint, one in each channel). Each channel is for some reason outputting the same result, an example is given of ...
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What is the difference between CNN-LSTM and RNN?

I'm starting to study RNN for a project of video prediction, but I encounter these CNN-LSTM models. Initially, I thought that is another name for RNN, but I think I get it wrong. Since I'm a beginner ...
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How are CNN kernels trained when using FFT for convolutions?

CNNs (convolutional neural networks) are adept at processing images, as their construction is based on the biological neural networks found in the human eye. "Kernels", sometimes called &...
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Convolutional network for multilabel classification in NLP

I am trying to label code snippets and I base on this article: https://arxiv.org/pdf/1906.01032.pdf My dataset is just code snippets (tokenized as ascii characters) and 500 different labels from ...
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What would be a good cost function based on both saliency-maps and labels?

I have a number of input samples where: every input sample has both a label and a reference-map. This reference-map gives a score to each location of an input sample. The score defines how much this ...
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What could be causing the poor performance (MSE) of a dense neural network on a real time-series dataset?

I am trying to understand time series analysis and actually I am following the course "Sequences, Time Series and Prediction" in Coursera. The course is based on a synthetic dataset, ...
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How exactly is modulation and demodulation layer in StyleGAN2 implemented?

So from the paper Analyzing and Improving the Image Quality of StyleGAN We know that the naive way to implement the stylegan2 Conv2DMod is to compute the Style vector which has the dimension of ...
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Appropriate machine learning approach for object detection of connector

I am suppose to detect a female connector which happens to be an automotive part. I need to draw a bounding box around the connector when it appear. Here's the closest resemblance part I could find to ...
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How do I compute the convolution of two kernels of the same size in practice?

Suppose I have a 256-by-256 input matrix called $X$ and two 3-by-3 kernels called $K_1$ and $K_2$. By the associativity of convolution \begin{equation} (X \star K_1) \star K_2 = X \star (K_1 \star K_2)...
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How to create a dataset for binary classification

I would like to classify whether a pot of water is boiling or not using a CNN. Is it enough to take pictures of boiling water using only one pot, or should I use different pots for this to generalize ...
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Strange artifacts in autoencoder outputs

I'm training an autoencoder, that does not downsample images but processes them in the same size. For example, a 256x256 input will always be processed at 256x256 resolution, only the channels ...
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How to convert prediction probabilities of 2D images (initially 3D image) to 3D image predictions?

Classification: binary Model: CNN (ResNet50V2) During our research we've had 91x109x91 images (3-dimensional). We've used 2D CNN to train and evaluate our images and make predictions on labelled cases,...
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What is intended for 1x1 convolution for input images?

I'm reading this about Self-Attention GANs : https://sthalles.github.io/advanced_gans/ I'm trying to better frame what is intended about 1x1convolution for input ...
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Training strategy on continuous video stream with CNN-LSTM

I have videos that are each about 30-40 mins long. With the first 5-10 mins (at 60fps, can be down-sampled to 5fps) are one type of activity that would be categorized by label-1 and the rest of the ...
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Does Using the Same Background for Binary Classification Improve Model Accuracy?

I am training a CNN that detects if a there is a pot of boiling water vs if there is a pot of boiling water with pasta inside. My hypothesis is that having the same background for both a positive and ...
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What is exactly sparse annotation?

What is exactly sparse annotation? Is it different from labeling images? I've been reading a paper about vessel segmentation and have some issues understanding this part.
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Is AI able to detect major changes in pairs of images while ignoring minor changes (due to tree crown growth, color and perspectiv disstortions)?

I'm starting to get involved into machine learning but still have some troubles to select the approriate tool or algorithm. My basic task is to compare remotly sensed images of individual trees at two ...
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Does it make sense to compare images (samples) with words (features)?

Consider the following paragraphs from the introduction of the chapter named Recurrent Neural Networks from the textbook titled Dive into Deep Learning So far we encountered two types of data: ...
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Is this aggregation of multiple convolutions of the same input a type of attention or dynamic convolution?

Are there any examples of people performing multiple convolutions at a single depth and then performing feature max aggregation as a convex combination as a form of "dynamic convolutions"? ...
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What can be the reasons for validation MSE < training MSE at beginning of training and network failing to generalize afterwards?

I am using a Convolutional Neural Network for regressing time series data. The objective is to predict an obfuscated metric. The training metrics and losses are as follows. The val_loss is lower than ...
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Avoid unintentional "merging" in cluttered object detection

I have a problem that has bothered me quite some time. With modern methods object detectors can often be accurately trained, even with small to medium sized datasets. However, there is one thing where ...
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Are there some known neural networks that, given an input image, can generate a similar image, with the same topic?

Are there some known neural networks that, given an input image, can generate a similar image, with the same topic? Example: input = a photo of a cat on a green table, output = a generated photo of ...
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DNN architecture for regression with large output layers

I'm facing a supervised regression problem where I need to predict the outcome of the numerical simulation of a physical process. Each simulation-sample outputs a 256x256x6 tensor of real numbers ...
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Image-in image-out neural network architectures

With an RGB image of a paper sheet with text, I want to obtain an output image which is cropped and deskewed. Example of input: I have tried non-AI tools (such as ...
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What is meant by non-linearity in Convolutional Neural Networks? And why do we focus on removing it entirely? [closed]

I'm aware of the working of ReLU that it's turns every negative value to zero and doesn't effect any positive value, but what confuses me is that: what is actually meant by Non-linearity in feature ...
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Why CNN inference works on larger images

I have been reading up on 'regular' CNN's such as Mask R-CNN, and as far as I understand it they rely on a fully connected layer in the end to classify pixels. FCN's (such as U-Net) which do not use ...
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Does the number of biases in a Convolutional layer scale with the number of images?

I am convolving 32 grey scale images of size 28 x28 with 16 filters of size 5x5. Which of the following is the correct way to add biases to the convolution operation output? add 1 scalar value bias ...
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Possible improvements to WGAN-GP output images

I am mapping rather complex data into what essentially amounts to a greyscale image to take better advantage of GANs for generative means. Here is an example of some real data: All real data is of ...
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Question about true positive and false positive detections in object detection

I am computing true positives, false positives, and false negatives in order to calculate my model's precision and recall. I am using YOLOv5. According to this source and this one, an IoU overlap ...
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Why do I have a shape mismatch when running back propagation of convolutional layer in CNN?

I am having problems with my array shapes when calculating change in loss wrt Kernel (delL/delk) in back propagation of convolutional layer. I am running a mini batch neural network operating on 32 ...
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why using ReLU in last two layes (flatten and fc)?

The reason of using ReLU is to somehow make the output positive(and add non-linearity). But why we need to have ReLU in last layers when we do not have any convolution to get negative values? (Assume ...
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Is there any subtle difference between kernel and filter in the context of neural netowrks?

Consider the following excerpt from a paragraph, taken from the topic Detecting features with convolutions of the textbook named Deep Learning with PyTorch by Eli Stevens et al., regarding ...
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Order of multiple Convolutional and Pooling layers in generated CNNs

I am reading this article: https://www.sciencedirect.com/science/article/pii/S2210650221000249 There, a multi layered particle swarm optimization of CNN parameters is presented. First step (layer) is ...
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Additional Optimizations for Convolutional Models On Inferencing

I am aware of several ways to optimize a convolutional (or any) model after training to make inferencing quicker. I am currently implementing BatchNormalization Folding and removing Dropout layers ...
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Confusion Matrix Measures vs Accuracy level in Neural Network Model

I'm working on a classification machine learning problem with two classes: high and low, which are derived from another numerical column x. Previously, if x>100, the sample is considered ...
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What's the best model to use for CNN(deep learning) regression task for small image dataset?

What are the best Deep learning models(with how many layers) to use in a regression task for a custom dataset containing around 100 images of only one object per image which is more or less ...
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3 votes
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YOLO - are the anchor boxes used only in training?

another question in YOLO. I've red about how YOLO adjusts anchor boxes by offsets to create the final bounding boxes. What I do not understand, is when YOLO does it. Is it being done only during the ...
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YOLO - does the Intersection over Union is actually a part of Non Maximum Suppresion

In the Stack Overflow thread Intersection Over Union (IOU) ground truth in YOLO they say that in YOLO actually the IoU (intersection over union) is used twice: during training to compare ground truth ...
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