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As you are in search of exploding / vanishing gradients, it would be the best to check the gradient histogram, rather than the weights directly. I found a code on Quora, pasting it just in case the link is gone with tf.name_scope('train'): optimizer = tf.train.AdamOptimizer() # Get the gradient pairs (Tensor, Variable) grads = optimizer.compute_gradients(...


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This is quite easy to do using the keras functional API. Assuming you have an image of size 28 by 28 and 5 additional features, your model could look something like this: from tensorflow.keras import Model, Input from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, concatenate input_image = Input(shape=(28, 28, 3)) input_features = Input(...


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I would look into what learning rate scheduler they are using. This seems like an effect of reducing lr based on a Cosine or ReduceOnPlateau strategy. See a figure from Loshchilov et al.


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You are finding about Semi-supervised object detection algorithm and Weakly-supervised object detection. Semi-supervised object detection uses Supervised-learning term (Your handmade labeled data) and Semi-supervised learning term (Unlabeled data). Weakly-supervised object detection uses coarse-grained data which is imperfect, inaccurate, or partial. For ...


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I went through the ZFNet paper and it seems that Deconvnet is different than transposed convolution. However, the idea of both is quite similar and it is easy to get confused. First, let's be clear about transposed convolution. There are many recent blogs and papers about it and here is one nicely written blog: https://towardsdatascience.com/what-is-...


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In regards to your question: However, they do not mention how this could then be used in the inference stage, because the required threshold for selecting the correct labels is not clear. Does anyone know how this would work? While this answer may be unsatisfying, I believe the answer is: you don't use it for inference. The paper describes how the multi-...


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YOLO is an object detection algorithm, considering your usecase of recognising alphanumeric characters it would be ideal to go for OCR(optical character recognition) which works great for written and handwritten characters. It's also ideal to opt for text detectors like EAST or CRAFT. I would suggest to go for keras OCR which is a packaged version of CRAFT ...


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