Starting the Journey of RNN, Commonly Known Four Structures of RNN
One to One: This is the traditional application of neural networks, usually used for simple input to output tasks. For example, in image classification, the network receives an image as input and identifies the category of the object represented in the image. Specifically, suppose you have a neural network model that can distinguish different animals. When you input a picture of a dog, the model analyzes the image and outputs a label like “dog”, which is a one-to-one mapping.
One to Many: This pattern is suitable for scenarios where a single data point generates a data sequence. A typical example is image captioning. In this application, the model receives an image and outputs a text description of the image content. For instance, for an image showing a sunset on the beach, the model might generate a series of words like “a beautiful sunset at the beach”. Here, a single image input is transformed into a sentence containing multiple words.
Many to One: This structure is used to simplify a data sequence into a single output, commonly seen in sentiment analysis. For example, the model reads a piece of text (like a movie review or social media post) and analyzes the sentiment tendency, ultimately outputting a score or category representing the overall sentiment (positive, neutral, or negative). In this case, a series of words (multiple inputs) are processed and summarized into a single sentiment label.
Many to Many: Many to many actually divides into two situations
(1) Equal Sequence Length:This application is usually seen in tasks that require classification or labeling of each part of the input sequence. For example, in frame-by-frame video processing, the input is a series of frames in a video. RNN analyzes each frame and generates labels for each frame, such as specific actions in behavior recognition. Here, each input frame corresponds to an output label.
(2) Unequal Sequence Length:A typical application is machine translation. In this case, the input is a text sequence in one language, and the output is a text sequence in another language. For example, if you have a translation model from English to French, when you input a piece of English text, such as “How are you today?”, the model processes this sequence and produces a French sequence as output, such as “Comment vas-tu aujourd’hui?”. The challenge here is that the lengths of the input and output sequences are often different, and the model needs to understand the semantics of the source language and accurately express the same meaning in the target language.