Let’s tackle your two questions one by one—they’re really common when getting started with these architectures, so great call asking!

1. Does the Encoder Participate in Training? #

Short answer: Absolutely, the encoder is fully involved in the learning process during training. It’s not a static feature generator that just outputs a feature map and calls it a day.

Here’s how it works:

• When you train an encoder-decoder model (like Seq2Seq for text, U-Net for image segmentation, or transformers), the entire pipeline is trained end-to-end. That means the loss calculated from the decoder’s output is propagated backward through the decoder, then through the encoder, updating all the weights in both components.
• The encoder’s job is to transform input data into a meaningful, compact representation (the "context" or feature map) that the decoder can use to produce the desired output. If the encoder’s weights weren’t updated, it might generate subpar features that make it impossible for the decoder to learn effectively. For example, in a machine translation model, the encoder needs to learn to capture the semantic meaning of the input sentence—this requires adjusting its embedding layers, attention weights, or convolutional filters (depending on the architecture) over time.
• The decoder doesn’t just "approximate results" in isolation; it relies on the encoder’s evolving features, and both parts adapt together to minimize the overall loss.

2. Are Filters Equivalent to Nodes in Traditional ML Models? #

Not exactly, but they share some core similarities in how learning happens. Let’s break the comparison down:

Similarities: #

• Both are driven by adjustable weights that are updated via backpropagation to minimize loss. Whether it’s a neuron in a fully connected layer or a convolutional filter, the goal is to learn patterns that help the model make better predictions.
• Both apply a transformation to input data (weighted combination + activation function, in most cases).

Key Differences: #

• Receptive Field: Traditional ML nodes (e.g., in a dense layer) take input from all previous layer nodes—they have a global receptive field. Filters in CNNs (the most common use case for "filters" in encoder-decoder models) operate on a local receptive field: they only look at a small subset of the input (like a 3x3 patch of an image) at a time.
• Weight Sharing: Filters use weight sharing—same set of weights is applied across the entire input space (e.g., an edge-detection filter slides over every part of an image). Traditional dense layer nodes have unique weights for each input connection, no sharing.
• Purpose: Filters are designed to learn spatial or temporal local patterns (edges, textures in images; phonemes in audio). Traditional nodes are more focused on combining global features to make decisions.

So while they both learn via weight adjustment, filters are specialized for local, structured data, whereas traditional ML nodes are general-purpose for flat, global feature combinations.

内容的提问来源于stack exchange，提问作者Kenneth Caselli