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Generative Adversarial Network (GAN) is a deep learning model proposed by Ian Goodfellow et al. in 2014. The framework implements generative modeling by training two neural networks against each other...

Self-Attention is a key mechanism in deep learning, originally proposed and widely used in the Transformer architecture. The core idea is to allow the model to simultaneously attend to all positions in the input sequence, and compute each position by weighted aggregation of...

Multi-Task Learning (MTL) is not an isolated algorithm, but an intelligent machine learning paradigm.

Diffusion Model (Diffusion Model) is a generative model specialized for creating new data samples such as images, audio or text. The core of the model is inspired by the process of diffusion in physics, which simulates the natural diffusion of particles from a region of high concentration to a region of low concentration. In the machine...

Model fine-tuning (Fine-tuning) is a specific implementation of transfer learning in machine learning. The core process is based on pre-trained models, which utilize large-scale datasets to learn generic patterns and develop extensive feature extraction capabilities. The fine-tuning phase then introduces task-specific datasets to ...

Attention Mechanism (Attention Mechanism) is a computational technique that mimics human cognitive processes, initially applied in the field of machine translation, and later becoming an important part of deep learning.

The Transformer architecture is a deep learning model designed for processing sequence-to-sequence tasks such as machine translation or text summarization. The core innovation is the complete reliance on self-attention mechanisms, eschewing traditional loops or convolutional structures. Allowing the model to process all elements of a sequence in parallel, large...

Pre-trained Model is a fundamental and powerful technique in the field of Artificial Intelligence, representing machine learning models that are pre-trained on large-scale datasets. Models form a broad knowledge base by processing massive amounts of information and learning generalized patterns and features from the data...

Large Language Model (LLM) is a deep learning system trained on massive text data, with the Transformer architecture at its core. The self-attention mechanism of this architecture can effectively capture long-distance dependencies in language. The model's "large ...

Long Short-Term Memory (LSTM) is a recurrent neural network variant specialized in processing sequence data. In the field of artificial intelligence, sequence data is widely used in tasks such as time series prediction, natural language processing and speech recognition.