Lstm vs gan Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that can predict sequences more accurately than conventional RNNs. Jun 19, 2023 · Conclusion: RNN, LSTM, GRU, GPT, and BERT are powerful language model architectures that have made significant contributions to NLP. LSTM: Network State Prediction | Find, read and cite all the research you need on ResearchGate Developed a robust airfare prediction system combining generative adversarial networks and LSTM to forecast dynamic ticket prices by analyzing historical data . While long short-term memory (LSTM) networks have emerged as the predominant solution for Vs prediction by synthesizing contextual relationships among conventional logging curves, existing implementations often overlook characteristic discrepancies Jun 24, 2025 · Shear wave velocity (Vs) serves as a crucial petrophysical parameter for subsurface characterization, yet its acquisition remains challenging. Sep 25, 2023 · RNN vs. Machine learning (ML) is a field that focuses on the development and application of methods Jul 5, 2024 · RNN vs. Long Short-Term Memory (LSTM) Networks using PyTorch LSTMs are widely used for sequence modeling tasks because of their ability to capture long-term dependencies. Jan 1, 2024 · For the generation of synthetic data that replicates real data, the integration of advanced GAN algorithms in combination with LSTM network architectures offers a novel approach to address the challenges described above. LSTMs have special gates to control the flow of information: Input Gate: Decides what new information to store. Jul 4, 2024 · ANN vs. This method produces different predictions in contrast to the single average behavior predicted by the S-LSTM. While S-LSTM exhibits superior performance in challenging sequential modeling problems, it Therefore, this paper focuses on the anomaly detection of time series data with the fusion model of LSTM and GANs, which is named the LSTM-GAN, and the performance of the algorithm is verified from two sets of time series data. Jan 3, 2023 · Request PDF | On Jan 3, 2023, Gyurin Byun and others published GAN vs. Feb 9, 2020 · RNNs, LSTMs, CNNs, Transformers and BERT Recurrent Neural Networks (RNNs) RNNs do have memory to keep track of things, so they allow information to be persistent over the network. This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Jul 3, 2020 · Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. Each model offers something unique: RNNs excel in crafting fluent narratives, LSTMs handle stories with intricate timelines, and GANs introduce a dash of creativity and novelty [12, 13]. n, a tree-structured LSTM network, known as S-LSTM, was proposed by [74]. They are effective in tasks such as image . In order to overcome the limited size of the original dataset, various data augmentation methods were tested (Gaussian perturbation, autoregressive modeling, LSTM Autoencoder, and Generative Adversarial Networks – GAN). Sep 13, 2023 · The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf Aug 14, 2019 · The Long Short-Term Memory recurrent neural network was developed for sequence prediction. LSTM: Understanding the Differences in Neural Networks Introduction Neural networks come in various architectures, each designed to handle different types of data and tasks … Apr 24, 2025 · Video Explanation This video is part of the ‘Introduction to Deep Learning’ course at MIT. Jun 24, 2025 · Shear wave velocity (Vs) serves as a crucial petrophysical parameter for subsurface characterization, yet its acquisition remains challenging. It has ability to forget, update and add context aided by the following 3 operations within a LSTM cell. CNN vs. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. After completing this post, you will know: About generative models, with a focus on […] Integrating LSTM layers into the GAN architecture significantly enhances the model's ability to generate realistic images from textual descriptions. The generator in GAN consists Jul 23, 2025 · Long Short-Term Memory (LSTM) Networks LSTM networks are an improved version of RNNs designed to solve the vanishing gradient problem. GRU: A Comprehensive Guide to Sequential Data Modeling Introduction In the realm of machine learning, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs Jul 21, 2023 · A Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model is used to learn the spatio-temporal dynamics of actions, then a GAN-based technique is applied to generate synthetic CNN-LSTM features conditioned on action classes which provide a high discriminative spatio-temporal features. Oct 1, 2020 · The GAN-CLS algorithm, which is an advanced method of GAN, can generate corresponding images from an input label description. Apr 12, 2024 · Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models. Learn the ins and outs of CNNs and GANs. May 2, 2024 · The two main components of a GAN are as follows: Generator: The generator network is responsible for creating new samples. The LSTM layers enable both the generator and discriminator to better understand and utilize the sequential nature of text data, leading to improved performance and higher quality image generation. LSTM vs. GAN: How are they different? Convolutional neural networks and generative adversarial networks are both deep learning models but differ in how they work and are used. LSTM What's the Difference? CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) are both popular types of neural networks used in deep learning. Significant part of LSTM is the memory that runs as a horizontal line at the top which carries the context. May 27, 2023 · Abstract page for arXiv paper 2305. Dec 3, 2020 · LSTM has a similar control flow as RNN but key difference being that the operations carried out within the LSTM cells. [7], which extended the S-LSTM by adding adversarial training with a GAN architecture. It takes random noise vectors as input and generates output samples, such Sep 15, 2024 · Long Short-Term Memory (LSTM) Networks: This is a type of RNN, designed to address the vanishing gradient problem, allowing them to learn long-term dependencies in sequential data. There are already many posts on … Neural Networks come in many different types. Output Gate: Decides what May 7, 2024 · Integration of LSTM and GAN model is the technique that is used for the synthetic generation of data, leading to the expansion of the training datasets, and as a result, the detection accuracy is improved. However, this method needs to find the best mapping from real-time space to the latent space at the anomaly detection stage, which Mar 21, 2019 · Understanding Deep Learning: DNN, RNN, LSTM, CNN and R-CNN Deep Learning for Public Safety It’s an unavoidable truth that violent crime and murder is increasing around the world at an alarming … Aug 13, 2023 · In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting. transformer models: Comparing architectures and uses Discover the differences between generative adversarial networks and transformers, plus how the two techniques might combine in the future to provide users with better results. They have enabled advancements in tasks such as language Aug 31, 2022 · A novel LSTM-GAN algorithm for time series anomaly detection. It is introduced by Ian Goodfellow and his team in 2014 and they have transformed how computers generate images, videos, music and more. Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. Forget Gate: Chooses what information to remove. Transformer: A Comprehensive Comparison Deep learning has revolutionized various domains, from computer vision to natural language processing (NLP), driving advancements Apr 12, 2024 · Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models. Unlike traditional models that only recognize or classify data, they take a creative way by generating entirely new content that closely 1. Nov 14, 2020 · RNN vs GRU vs LSTM In this post, I will make you go through the theory of RNN, GRU and LSTM first and then I will show you how to implement and use them with code. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. In this lecture, Professor Ava Amini delves into the concepts of Sequence modeling, and covers the full gamut of sequence models including RNN, LSTM and Transformers. A comparison is also Dec 1, 2021 · The most important data-driven work to address multimodality is the Social-GAN (S-GAN) by Gupta et al. In addition to sequence prediction problems. Look at the Oct 9, 2025 · In this article, we will learn how to implement an LSTM in PyTorch for sequence prediction on synthetic sine wave data. Apr 2, 2025 · CNN vs. Transformers: Unraveling the Secrets of Sequential Data Processing In the realm of deep learning, sequential data processing is at the heart of many applications, including I benchmarked it vs LSTM, Dense linear, and exponential smoothing and it outperforms them (quite significantly in some cases). CNNs are primarily used for image and video processing tasks, as they are designed to automatically learn and extract relevant features from input data through convolutional layers. Mar 26, 2024 · The range of techniques that RNN, LSTM, and GAN models bring to the table enriches the process of story creation [10, 11]. In Proceedings of the 2019 Prognostics and System Health Management Conference (PHM-Qingdao’19). Introduction Artificial intelligence (AI) aims to emulate human-level intelligence in machines. In computer science, AI refers to the study of "intelligent agents," which are objects capable of perceiving their environment and taking actions to maximize their chances of achieving specific goals [1]. However, I have a quite a data-intensive task, so scaling up to a more complex NN has helped. LSTMs can also be used as a generative model In this post, you will discover how LSTMs can be used as generative models. RNN vs. Furthermore, we will utilize Generative Adversarial Network (GAN) to make the prediction. Mar 17, 2021 · Also, a new state-of-the-art ensemble GAN/LSTM deep learning architecture is proposed to predict each sub band workload time-series individually, based on its degree of complexity and volatility. Mar 3, 2025 · CNN vs. While long short-term memory (LSTM) networks have emerged as the predominant solution for Vs prediction by synthesizing contextual relationships among conventional logging curves, existing implementations often overlook characteristic discrepancies In the given work, we demonstrate a comprehensive system that consists of data preparation, LSTM modelling, GAN modelling, LSTM data augmentation, and LSTM retraining - all of them being important for improving the malware detection sys-tem. This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Sep 13, 2025 · Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. There are 6 main types of neural networks, and these are the ones you need to know about. 17473: A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU Oct 9, 2025 · Generative Adversarial Networks (GAN) help machines to create new, realistic data by learning from existing examples. This presentation offers valuable insights into the conceptual understanding, advantages, limitations and use cases of each model Jan 28, 2025 · GAN vs. They use memory cells that keep information over longer periods. S-LSTM consists of memory blocks co prising an input gate, two forget gates, a cell gate, and an output gate. - lokesh705/AI-Flight-price-predic This work presents a hybrid approach for forecasting and anomaly detection in time-based transactional data, using advanced deep learning techniques. bglqa lamyeu dp rijs avgk hxpl k7x 0pdeuy9c egapes 3lg