Transformers — Encoder Decoder Architecture Pros and Cons(Part 4)
Part 1 : Transformers Encoder Decoder — Input and Positional Encoding
Part 2 : Transformers Encoder Decoder — Encoder Stage
Part 3 : Transformers Encoder Decoder — Decoder Stage
Part 5 : Research Papers related with Encoder Decoder
Transformer Encoder decoder architecture က Natural Language Processing နဲ့ Computer Vision ပိုင်းမှာ အသုံးများပြီး application တွေနဲ့ အနေဆို Machine Translation ၊ Image Captioning၊ Text Summarization တွေမှာ သုံးတာ နာမည်ကြီးပါတယ်။
There are several advantages to implementing an encoder-decoder architecture with Recurrent Neural Networks (RNNs):
- Flexibility: Encoder-Decoder RNNs can be used for various tasks, such as machine translation, text summarization, and image captioning.
- Handling variable-length input and the output: Encoder-Decoder RNNs are particularly useful for tasks with different lengths of input and output sequences.
- Handling sequential data: RNNs are particularly well-suited for handling sequential data.
- Handling missing data: Encoder-Decoder RNNs can handle missing data by only passing the available data to the encoder.
Disadvantages of Encoder-Decoder RNNs
There are also some disadvantages to implementing an encoder-decoder architecture with RNNs:
- Long-term dependencies: RNNs can struggle to capture long-term dependencies in the input data, which can be an issue for some tasks.
- Training difficulties: Training an RNN can be challenging, particularly when the input and output sequences are of different lengths.
- Computationally expensive: RNNs can be computationally expensive to train, especially for large datasets.
- Overfitting: RNNs are prone to overfitting, particularly when the dataset is small, which can be an issue for some tasks.
Further Reading
Happy Learning
