Exploring Encoder & Decoder Types

An Encoder-Decoder is a neural network architecture primarily used in sequence-to-sequence (seq2seq) tasks where input and output sequences can have different lengths. The model consists of two parts:

1. Encoder: Processes the input sequence and compresses it into a fixed-size context vector (also called the hidden state or latent representation). This vector captures the important information (context, semantics) from the input sequence.

2. Decoder: Takes the context vector and generates the output sequence

   The output may vary based on the applications such as :

• Machine Translation: Translating text from one language to another (e.g., English to Hindi)

• Image Captioning: Generating descriptive captions for images

Types of Encoder-Decoder Architectures

1. Basic RNN Encoder-Decoder:

   • Uses Recurrent Neural Networks (RNNs) for both encoder and decoder.

   • Limitations include vanishing gradient problems and difficulty in handling long sequences.

2. LSTM/GRU Encoder-Decoder:

   • Uses Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) networks to address RNN limitations.

   • Better at capturing long-term dependencies in sequences.

3. Attention-based Encoder-Decoder:

   • Introduces an attention mechanism to focus on different parts of the input sequence when generating each output element.

   • Improves performance, especially in tasks like machine translation, by allowing the model to selectively attend to parts of the input sequence.

4. Transformer based Encoder-Decoder:

   • Uses self-attention mechanisms in both encoder and decoder, without relying on RNNs or CNNs.

   • Highly parallelizable and performs well on a variety of seq2seq tasks.

Variants in Transformer Based Architectures:

1. Encoder-Decoder Models: These models use both the encoder and decoder components of the Transformer as discussed above, suitable for sequence-to-sequence tasks where the input and output sequences may differ in length or structure.

   Examples: Original Transformer (Vaswani et al.), T5, BART

2. Encoder-Only Models: These models use only the encoder part of the Transformer to process input data and are typically used for tasks like classification, regression, and extraction.

   Examples: BERT, RoBERTa

3. Decoder-Only Models: These models use only the decoder part of the Transformer, primarily for generating sequences based on a given prompt or context.

   Examples: GPT, GPT-2, GPT-3, GPT-4

In the next article, I will deep dive into Attention Based Encoder-Decoder Model in Depth with Mathematical Explanations as simply as possible!

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