I didn't understand it. End of the day for even sized filter you could consider any pixel to be the center pixel right? it will end up giving similar values though not same. also max pooling and average pooling works on a output feature map so how is it related?

There are Equivalents in AI Model Development and Inference for those though. Many of these signal processing techniques have analogs or are directly applicable in AI and machine learning: - **FFT, FHT, DCT, and MDCT:** Used in feature extraction and preprocessing steps for machine learning models, especially in audio and image processing. - **FIR and IIR Filters:** Used in preprocessing steps to filter and clean data before feeding it into models. - **Wavelets and Wavelet Transforms:** Applied for feature extraction and data compression, useful in handling time-series data. - **Compressive Sensing and Sparse Reconstruction:** Important in developing models that can work with limited data and in reducing the dimensionality of data. - **SIFT, SURF, BRISK, and FREAK:** Feature detection and description techniques that are foundational in computer vision tasks like object recognition and image matching. In AI, techniques like convolutional neural networks (CNNs) often use concepts from signal processing (like filtering and convolutions) to process data in a way that mimics these traditional methods. Signal processing principles help in designing more efficient algorithms and models, improving performance in tasks such as image recognition, speech processing, and time-series analysis.

Yes, I imagine that many AI students that only see 2D animations are surprised to learn the 2D convolutions actually work with 3D tensors (or 4D if it's batched). That was one of my main motivations for creating these animations :)

I'm planning to make a future video on the math that will go over this in detail. For now, let's just look at the number of parameters needed. (Total floating-point operations is roughly correlated with number of parameters, but we would need to also consider stride to calculate it precisely.) You can calculate the parameter count with filter_height * filter_width * filter_count * input_feature_count. Note that we need to include the filter_count of our layer. A realistic first layer might be 7x7 with 64 filters. So the parameter count would be 7*7*3*64 = 9,408. We can compare that to a stack of 3x3 layers with filter counts of 16, 32, and 64. Their parameter count would be 3*3*3*16 + 3*3*16*32 + 3*3*32*64 = 23,472. Another possibility might be a 5x5 layer with 32 filters. It's parameter count would be 5*5*3*32 = 2,400. We could compare that to a stack of 3x3 layers with filter counts of 16 and 32. Their parameter count would be 3*3*3*16 + 3*3*16*32 = 5040. If the first layer in the 3x3 stack had a filter count less than 8, then the stack would be more efficient. However, I haven't seen a filter count that low in practice.

@@animatedai Thank you very much for the examples and the explanation! The video is also very solid!

Yes, that's correct

Check out my reply to this comment for an explanation: kzfaq.info/get/bejne/jJ-KjKd006_YomQ.html&lc=UgweJZ_Bri8emvyNAMF4AaABAg.9hOBaZTROlX9hQHXzxdOZM

I've been trying to avoid equations in the videos, but the formula for the total number of weights needed is (filter width * filter height * filter count * input feature count). You can see this represented visually in my filter count video. Assuming that the filter count is the same as the input feature count, it's more efficient to break large (5x5, 7x7, ...) filters into multiple 3x3 filters. A concrete example where all inputs and outputs have F feature dimensions: One 7x7: (7 * 7 * F * F) = 49 * F^2 Three 3x3s: (3 * 3 * F * F) + (3 * 3 * F * F) + (3 * 3 * F * F) = 27 * F^2 But for the first layer, the filter count is usually much higher than the input feature count of 3. It's more efficient to perform this dramatic increase in feature count using one large filter than with multiple smaller one. A concrete example where the first layer has 16 filters: One 7x7: (7 * 7 * 3 * 16) = 2352 Three 3x3s: (3 * 3 * 3 * 16) + (3 * 3 * 16 * 16) + (3 * 3 * 16 * 16) = 5040

Thanks for taking the time to explain this!@@animatedai

The filters in the first layer don't need to be larger. There's just no performance benefit to splitting them into a chain of smaller filters. And the reason for that is that the number of features increases dramatically from the input (typically 3 channels for RGB) to something like 16 or 32. The performance benefit of splitting a large filter into smaller filters assumes the number of features stays the same from input to output. > And what is better, more chained filters with lower channel count, or lesser amount of chained filters with more channels? This really depends on the data, how long the chain is, and how many filters you have. It's an ongoing area of research where researchers have found great results in both cases.