It Supports CNN and RNN or similar (GRU, LSTM, etc...). Seq2Seq Model: Sequence to Sequence is a type of model that is specifically designed to handle sequences.
Thank you for the answer. However, I'm still struggling to understand the purpose of the bounding box. As far as I know, object detection algorithms from R-CNN to YOLO all perform detection using the feature map (which is the output of a CNN). Shouldn't that mean that the computations for the convolutions are not affected regardless of the bounding box (because they are always computed)?
What does it mean by "low-level" and "high-level" in features generated by CNN? Ask Question Asked 4 years, 3 months ago Modified 3 years, 11 months ago
I'm having trouble understanding how bias is added to the feature extraction convolution. I've seen people either refer to the bias as a single number that changes per filter or the whole matrix th...
CNN is suitable for forecasting time-series because it offers dilated convolutions, in which filters can be used to compute dilations between cells. The size of the space between each cell allows the neural network to understand better the relationships between the different observations in the time-series [14].
Background: I have a 2D CNN model that I am applying to a regression task with some uniquely extracted spectrograms. The specifics of the data set are mostly irrelevant and very domain specific so...
A CNN can be trained incrementally. For example, in the paper Incremental Learning of Convolutional Neural Networks, the authors propose an incremental learning algorithm (inspired by AdaBoost and Learn++, which is another incremental learning algorithm for supervised learning of neural networks) for CNNs.
So, the value of these filters is not fixed or pre-determined, but will depend on how you train the CNN, i.e. the learning algorithm, the objective function and the data. If you use gradient descent as the learning algorithm, you will be minimizing a loss (aka cost or error) function (e.g. the cross-entropy, in the case of classification problems).
Feature selection is a process of selecting a subset of features that contribute the most. Feature extraction allows getting new features that are not actually present in the given set of features.
