A deep learning framework where two neural networks, a generator and a discriminator, compete to improve image generation quality.

A model architecture that uses self-attention mechanisms, excelling in tasks like image classification and natural language processing.

A comprehensive survey of existing research, identifying trends, gaps, and methodologies relevant to a specific field.

The task of assigning labels to images based on their content, using various machine learning techniques.

A process in Transformer models that allows the model to weigh the importance of different parts of the input data.

The process of optimizing model parameters that are set before training, impacting model performance.

Quantitative measures used to assess the performance of machine learning models, such as accuracy and F1 score.

An area within a field that has not been fully explored or understood, providing opportunities for new research.

A formal document detailing research findings, methodologies, and implications, suitable for submission to academic journals.

A method of evaluating two or more models or approaches to determine their relative strengths and weaknesses.

The percentage of submitted papers that are accepted for publication in a journal, indicating its selectivity.

A quality control mechanism where experts evaluate the research paper before publication to ensure its validity and relevance.

Guidelines for properly referencing sources in academic writing, ensuring credit is given to original authors.

Additional content submitted alongside a research paper, such as data sets or code, to support findings.

A subset of machine learning that uses neural networks with many layers to model complex patterns in data.

Emerging patterns and developments in artificial intelligence that influence research directions and applications.

Techniques used to artificially expand training datasets by generating modified versions of existing data.

A method where a pre-trained model is adapted for a new, but related task, improving efficiency and performance.

The process of making small adjustments to a pre-trained model to improve its performance on a specific task.

Mathematical functions in neural networks that determine the output of a node, influencing learning.

A technique to improve training speed and stability in neural networks by normalizing layer inputs.

A modeling error where a model learns the training data too well, failing to generalize to new data.

A scenario where a model is too simple to capture the underlying structure of the data, resulting in poor performance.

A class of deep neural networks particularly effective for processing structured grid data like images.

The systematic approach employed in conducting research, including data collection and analysis techniques.