Lstm vae. v1. Parameters regarding to network (number of layers and dimensions) can be changed from this file. 5. Compared to the VAE and LSTM models alone A simple tutorial of Variational AutoEncoder(VAE) models. This repository contains the implementations of following VAE families. Nov 5, 2023 · In text processing, the traditional attention is generally used in the encoder-decoder of the sequence model Long Short Term Memory (LSTM) . 2. m) and the LSTM time series prediction. 시계열 예측에서 이상감지 하는 방안들을 찾던 중 알게 된 논문이고, LSTM, VAE, GAN에 대한 내용을 Dec 5, 2023 · The results show that the novel Bi-LSTM-VAE method can produce a non-overlapping distribution of phase points, indicating an effective unsupervised mode recognition and classification. KQNG ・ 2021. This detector fuses the fused time series signal, reconstructs its expected distribution by introducing a Jan 1, 2022 · LSTM-VAE (Park et al. content_copy. In contrast to the more standard uses of neural networks as regressors or classifiers, Variational Autoencoders (VAEs) are powerful generative models, now having applications as diverse as from generating fake human faces, to producing purely synthetic music. Variational AutoEncoder (VAE, D. Star 225. , 2018 ) assumes a Gaussian mixture prior in the latent space to replace the uni-modal Gaussian assumption of VAE. Notifications Fork 0; Star 1. ningchaoar / cnn-lstm-vae Public. C. Mar 13, 2023 · Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. A novel variational autoencoder for natural texts generation is presented in this paper. 14, the deviation areas of PCA-LSTM are similar to that of VAE-LSTM and the prediction errors of these two models are low. 視覚によるコミュニケーションというのは人々が相手に何らかのアイデアを伝える際に鍵となります．私たちは小さい頃から物体を描く力を養っ In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. py) To test the implementation, we defined three different tasks: Aug 7, 2022 · Time series prediction problems are a difficult type of predictive modeling problem. py: This file includes the VAE class, which includes the encoder and the decoder, the sampling method and the forward function. A key attribute of recurrent neural networks is their ability to persist information, or cell state, for use later in the network. However, those models of processing the sequence depend on the order between words and Jul 3, 2020 · 2. These models are the LSTM Variational Autoencoder (VAE_Outl_LSTM. is paper proposes a VAE-LSTM hybrid deep model based on a multihead self-at-tention mechanism, which integrates VAE and LSTM as a whole for unsupervised anomaly In this paper, we explores the use of machine learning algorithms for music generation and retrieval. This work is the first attempt to integrate unsupervised anomaly detection and trend prediction under one framework. A VAE is a probabilistic take on the autoencoder, a model which takes high dimensional input data and compresses it into a smaller representation. 𝜇 í and 𝜎 í are obtained by the linear transformation of the encoder output. 𝑊 5 and 𝑊 6 are the coefficients of the linear transformation. proposed a multimodal (the fusion of high-dimensional and heterogeneous modalities) anomaly detector LSTM-VAE based on an LSTM network and VAE. Shuyu Lin 1, Ronald Clark 2, Robert Birke 3, Sandro Schönborn 3, Niki Trigoni 1, Stephen Roberts 1. URL 복사 이웃추가. , 2013) Vector Quantized Variational AutoEncoder (VQ-VAE, A. Then Decoder takes the latent vector as input and converts it back to data. 620 respectively because of their similar This example shows how to create a bidirectional long-short term memory (BiLSTM) function for custom deep learning functions. "Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model. While with the VAE, an fnn_multiplier of 1 yielded sufficient regularization for all noise levels, some more experimentation was needed for the LSTM: At noise levels 2 and 2. The method has two stages, one is the model training stage and the other is the anomaly detection stage. Authors: Jonas Søbro Christophersen & Lau Johansson. Feb 4, 2018 · 54. The LSTM network training was done differently than the CNN-VAE. 000 to 149. e. Our method jointly trains the encoder, the generator and the discriminator to take advantage of the mapping ability of the Mar 19, 2024 · We propose a novel architecture for synthetically generating time-series data with the use of Variational Auto-Encoders (VAEs). We’ll use the MNIST dataset for validation. For evaluations with 1,555 robot-assisted feeding executions including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve (AUC) of 0. However, a major limitation of existing works is that they fail to jointly learn the local Jan 2, 2024 · 在这项工作中，我们提出了一种vae-lstm混合模型，作为一种无监督的时间序列异常检测方法。我们的模型既利用vae模块在短窗口上形成稳健的局部特征，又利用lstm模块在从vae模块推断的特征之上估计序列中的长期相关性。 Mar 3, 2024 · Now that we have an understanding of the VAE architecture and objective, let’s implement a modern VAE in PyTorch. LSTM was used. For a given dataset of sequences, an encoder-decoder LSTM is configured to read the input sequence, encode it, decode it, and recreate it. PyTorch VAE Implementation# Our VAE implementation is broken into an Output template (in the form of a dataclass), and a VAE class that extends the nn. Notifications. Specifically, Long Short-Term Memory (LSTM) and Variational Autoencoders (VAEs) are employed to generate music based on a monodic sound composition (MIDI file). In addition, for the problem that sometimes non-anomalous has a higher anomaly score, this Dec 3, 2023 · Meanwhile, CNN+LSTM and HyVAE in most cases produce more accurate forecasting results than other deterministic DNN-based methods and VAE-based methods, respectively, and that again supports the effectiveness of learning both local patterns and temporal dynamics for time series forecasting. The mean alarm time lag is the shortest for the LSTM-VAE compared with the other five detection methods for all noise With the development of graph applications, generative models for graphs have been more crucial. attention mechanism. These base models Nevertheless, I would like to share a few interesting things with the community. Let’s take a look at a more sophisticated model: LSTM-VAE, Variational Auto-Encoder (VAE) with LSTM encoder and decoder modules for representing time-series data. md ├── data │ ├── external => External Data(e. the company said it will be n with the exception of the company Nov 1, 2021 · The VAE-LSTM model is used to help correct and refine these proposed design drafts, respectively. The proposed model's t-SNE method aims to minimize the dimensionality of the recorded gas concentration; and VAE layer intends to retrieve the inner characteristics of low-dimensional gas concentration. Each LSTM-VAE network and threshold Mar 31, 2022 · In a (Beta-) VAE the loss is Loss = MSE + beta * KL . The network layer of the generator of the confrontation network is merged end to end. Apel Mahmud Abstract—The advancement in distributed generation tech-nologies in modern power systems has led to a widespread integration of renewable power generation at customer side. Contribute to carrtesy/LSTMVAE-Pytorch development by creating an account on GitHub. The encoder is comprised of a LSTM network and two linear neural networks to estimate the mean and co-variance of the latent variable z. As a result, our detection algorithm is capable of identifying Jun 1, 2023 · LSTM_VAE. We train the LSTM-VAE using multimodal signals and corresponding progress-based priors. As a result, our detection Dec 6, 2018 · I try to build a VAE LSTM model with keras. Input shape is (sample_number,20,31) While, there are some incompatible issue happening. This library comprises 16 LSTM-VAE networks that are trained for each known event (i. Moreover, this model performs considerably better on detection and Apr 22, 2021 · The effects of VAE are different. al. Shohei Nakazawa, Yoshiki Sato, Kenji Nakagawa, Sho Tsugawa, Kohei Watabe. See the code here: ├── README. Compared to the previously introduced variational autoencoder for natural text where both the encoder and decoder are RNN-based, we propose Mar 13, 2024 · Convolutional Variational Autoencoder. , 2017) Nov 2, 2017 · We also introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score and a state-based threshold. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. I tried to combine the above code with the ideas from this SO question that seems to deal with it the "best way" by cropping the gradients to get the most accurate loss as possible, however my Aug 27, 2020 · An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. The embeddings were fed to K-means clustering algorithm to group molecules based on their temporal patterns. , 2018) equips the LSTM cell in the VAE framework, showing VAE’s superiority of the probabilistic reconstruction in the continuous latent space. こんにちは．エクサウィザーズでインターンをしている川畑です．. The VAE block is in charge of anomaly detection and LSTM is adopted for prediction. Contribute to gangqing/EEG_VAE_LSTM development by creating an account on GitHub. py) LSTM-AE + prediction layer on top of the encoder (LSTMAE_PRED. 안녕하세요, 이번에 AI 스터디를 위해 준비한 내용을 블로그에도 공유해보려 합니다. Explore topics Improve this page Add a description, image, and links to the vae A VAE-Based Bayesian Bidirectional LSTM for Renewable Energy Forecasting Devinder Kaur, Shama Naz Islam, and Md. 5, that multiplier was set to 5. We then train a threshold estimator using the outputs of the LSTM-VAE. Module class. Star GitHub - twairball/keras_lstm_vae: Keras implementation of LSTM Variational Autoencoder. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term correlation in the series on top of the features inferred from the VAE module. LSTM-Based VAE-GAN This paper presents a LSTM-based VAE-GAN method for time series anomaly detection. Classically, stochastic models that generate graphs with a pre-defined probability of edges and nodes have been studied. The code implements three variants of LSTM-AE: Regular LSTM-AE for reconstruction tasks (LSTMAE. In this paper, we propose SeqVL (Sequential VAE-LSTM), a neural network model based on both VAE (Variational Auto-Encoder) and LSTM ( Long Short-Term Memory ). Nov 5, 2022 · LSTM-VAE : This method proposes a long–short-term memory-based variational autoencoder (LSTM-VAE), which can extract the time dependence of sequence data and achieve better performance than donut in KPI anomaly detection with partial time dependence. A powerful type of neural network designed to handle sequence dependence is called a recurrent neural network. To enhance the flexibility of VAE (learns independent latent random variables), follow-up methods introduce extra dependencies among the latent random variables. 600 to 0. 13:07. Notifications Fork 1; Star 3. Variational autoencoder VAE-LSTM for anomaly detection (ICASSP'20) Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model . For the cyclical data series, the effects of feature extraction in VAE seem to be similar to that in PCA. An overview of our model is shown below: Mar 7, 2021 · Trajectory VAE (TrajVAE) Variational AutoEncoder (VAE) [12] is comprised of two elements: Encoder and Decoder. LSTM, a neural network commonly used in deep learning and artificial intelligence, is utilized to train the initial music set The variational autoencoder(VAE) has been proved to be a most efficient generative model, but its applications in natural language tasks have not been fully developed. As a result, our Nov 9, 2018 · Sketch-RNN でスケッチの自動生成（VAE + LSTM）. Shell 0. 2021. Jul 31, 2020 · Results. We evaluate data generation quality by similarity and predictability against Sentenes have been obtained after sampling twice from z ~ N(0, I) and the interpolating the two samples. LSTM-VAE was employed to extract low-dimensional embeddings from time-series multi-omics data. This should help the reconstruction but is bad if you would like to have a disentangled latent space. disable_eager_execution() class VAE: def __ Sep 29, 2022 · In this. SyntaxError: Unexpected token < in JSON at position 4. Download scientific diagram | BiLSTM-VAE network structure. The Varaiable container is replaced by Tensor in this version. py) LSTM-AE + Classification layer after the decoder (LSTMAE_CLF. 4. The architecture of the model is shown in Figure 1. Variational autoencoder LSTMs for time series data. This post will explore what a VAE is, the intuition behind why it works so well VAE-LSTM for anomaly detection (ICASSP'20) This Github repository hosts our code and pre-processed data to train a VAE-LSTM hybrid model for anomaly detection, as proposed in our paper: Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model . master. As shown in Fig. Variational autoencoder (VAE) [14] is a powerful deep generative model that encodes the input data as latent ran-dom variables, instead of deterministic values. answered Apr 28, 2022 at 6:44. Marcelo A. Star Notifications Jul 5, 2022 · Regarding anomaly detection with LSTM and VAE (or autoencoder (AE)) hybrid models, Park et al. . This is a keras code for LSTM-based variational autoencoder (LSTM-VAE). Fig. This repository contains hand-in assignment for the DTU course 02460 Advanced Machine Learning. twairball / keras_lstm_vae Public archive. Explore and run machine learning code with Kaggle Notebooks | Using data from pump_sensor_data. The LSTM used for comparison with the VAE described above is identical to the architecture employed in the previous post. P. Dec 21, 2020 · In this post, we introduced an application of Variational AutoEncoder for time-series analysis. keyboard_arrow_up. On the other hand, LSTM helps VAE to maintain the long term sequential patterns that are out of the VAE encoding window. 5. LSTM-VAE. py at main · CUN-bjy/lstm-vae-torch Anomaly Detection using Variational Autoencoder LSTM. The architecture of all the models are kept as Nov 5, 2022 · LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly de tection. The main parts of this repository, that might be of interest, are the two developed models to detect anomalies in time series data. The optimization paths in latent space of these four examples are shown, plotted by their respective marker symbols in a-d, illustrating that all structures converge on the optimal curve after repeated iterations. Unlike a traditional autoencoder, which maps the input Jan 23, 2022 · I am solving a Timeseries problem using LSTM VAE(Variational auto-encoder), I have built my VAE model as below import tensorflow as tf tf. With the development of graph applications, generative models for graphs have been more crucial. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising forecasting results than deterministic models. Train function is not included in this file. LSTM Auto-Encoder (LSTM-AE) implementation in Pytorch. LSTM networks are a sub-type of the more general recurrent neural networks (RNN). 2: Illustration of a multimodal anomaly detector with an unrolled LSTM-VAE model. Fernandes. 4. LSTM-AE is an auto-encoder built from LSTM layers that learns a latent space representation for the input. This notebook is a implementation of a variational autoencoder which can detect anomalies unsupervised. 8710 than 5 other baseline Aug 4, 2021 · LSTM and Attention mechanism are used to solve long-term dependency problems when treating SMILES grammar inputs. This one has right implementation and cost function for batch training. " Forces in Mechanics, 5. Based on the comparison of five clustering methods, the LSTM-VAE method shows the best performance. A method for constructing hydraulic pump health indicators and evaluating health status is proposed based on LSTM–VAE. The performance of the model is evaluated based on the model’s ability to recreate Sep 16, 2022 · This paper addresses the difficulty of evaluating operating status in widely used gear pumps. In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. May 1, 2020 · This work proposes a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series and demonstrates the effectiveness of the detection algorithm on five real world problems and finds the method outperforms three commonly used detection methods. py: When this file is running: Datasets are loaded, they are The LSTM-VAE is trained using temporal proteomics and metabolomics to obtain integrated low-dimensional features, and then the k-means algorithm is used to cluster the low-dimensional features. Version: Final published version. Furthermore, the present method exhibits a more prominent performance than VAE and PCA (principal component analysis) for distinguishing dynamical modes in formation outside the window. On the one hand, VAE considerably reduces the impact from the anomalies and noise on the prediction block. For testing, we input sensory signals only. compat. LSTM-based VAE-GAN architecture. We built a VAE based on LSTM cells that combines the raw signals with external categorical information and found that it can effectively impute missing intervals. Such as DA-LSTM-VAE is a model, which attention is based sequence LSTM that the weight calculation needs target. They used the hybrid model to analyze the data collected through 155 If the issue persists, it's likely a problem on our side. Apr 22, 2021 · LSTM-VAE. Refresh. However, in these conventional 3 days ago · LSTM-VAE [28] restructures the training data using a Variational Auto-Encoder (VAE), where the encoder masters the probability distribution of the training data, and the decoder rebuilds the data using a representation sampled from the distribution that the encoder has learned. Oct 9, 2019 · In this paper, we propose SeqVL (Sequential VAE-LSTM), a neural network model based on both VAE (Variational Auto-Encoder) and LSTM (Long Short-Term Memory). paper, in order to discover the abnormal information in the QAR data, we applied a VAE-LSTM model with a multihead self-. Park et al. Most of the implementations on the internet are either wrong, or they do not work with batch size greater than 1, because their loss function is wrong. Encoder attempts to capture the distribution of training data and map it into a lower-dimension latent vector. Firstly, in order to capture time Firstly, in order to capture time correlation in KPI data, long–short-term memory (LSTM) units are used LSTM-VAE. The proposed architecture has several distinct properties: interpretability, ability to encode domain knowledge, and reduced training times. Firstly, the normalized feature vectors of the whole-life operation data of PHM aims to provide optimal maintenance schedule through the use of sensor measurement for fault detection and fault prognostics, among which fault detection is the first and fundamental action. In a deep learning model, a bidirectional LSTM (BiLSTM) operation learns bidirectional long-term dependencies between time steps of time series or sequence data. Kingma et. Deep LSTM Variational AutoEncoder. Recently, some models that reproduce the structural features of graphs by learning from actual graph data using machine learning have been studied. It is inspired by the approach Aug 21, 2019 · I am trying to implement a LSTM VAE (following this example I found), but also have it accept variable length sequences using Masking Layers. Contains Dense layer at the end to be able to produce model. 1 code implementation in TensorFlow. A Simple Pytorch Implementation of LSTM-based Variational Autoencoder(VAE) - lstm-vae-torch/main. An encoder in the VAE module Citation. The detector then returns an anomaly when current Our LSTM-VAE-based detector reports an anomaly when a reconstruction-based anomaly score is higher than a state-based threshold. Jan 1, 2024 · [22] embeds the correlation of time series into VAE to detect anomaly, in which encoder using LSTM as basic unit maps multimodal observations and their temporal dependencies at each time step into a latent space, and decoder with LSTM as basic unit estimates the expected distribution of the multimodal input from the latent space representation. 1 and 0. 9%. Our model is trained on the normal time series data to learn the distribution of them at the Jan 28, 2021 · The final LSTM unit is activated by a linear function as the first output for the LSTM sequence encoder part. e. In short, our anomaly detection model contains: a LSTM model, which acts on the low- dimensional embeddings produced by the VAE model, to manage the sequential patterns over longer term. For evaluations with 1555 robot-assisted feeding executions, including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve of 0. 10. 基于VAE_LSTM的脑电信号异常检测. Then GGM-VAE ( Guo et al. from publication: A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD | Key performance VAE + LSTM models. Apr 15, 2021 · A Tunable Model for Graph Generation Using LSTM and Conditional VAE. Since beta = 1 would be a normal VAE you could try to make beta smaller then one. Unexpected token < in JSON at position 4. The detector then returns an anomaly when current Fig. Implemented in Keras. g word vectors) │ ├── interim => Cleaned data before undergoing writing to tfrecord │ ├── processed => tfrecord files for train/eval and vocab │ └── raw => Downloaded or collected data ├── docs => Contain some notes on the implementation ├── results => Output of experiments May 1, 2019 · This indicates that except for RNN-AE, the corresponding PRD and RMSE of LSTM-AE, RNN-VAE, LSTM-VAE are fluctuating between 145. May 1, 2020 · VAE-LSTM [8] uses both a VAE module for identifying local features of a short window and an LSTM module for estimating the general correlation in the long term. Similar to LSTM AE model, LSTM-VAE is also a reconstruction-based anomaly detection model, which consists of a pair of encoder and decoder. In parallel to the self-attention encoder sequence, the second branch of the model is an encoder of VAE. Secondly, a pretrained model was generated using the data from the Healthy group to create base models for the CNN-VAE and the LSTM networks. In this study, the vibration signal data source of gear pumps was assessed in the accelerated life test. jaanli / vae-lstm Public. For this task, the dataset was segmented into a four-dimensional array (n samples _ l s t m, n w i n, l e n w i n, n channels) according to Section 2. Our model Apr 1, 2021 · To implement this function, an LSTM-VAE library was developed. VAE-LSTM for anomaly detection (ICASSP'20) This Github repository hosts our code and pre-processed data to train a VAE-LSTM hybrid model for anomaly detection, as proposed in our paper: Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model . Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. Unsupervised Deep Learning for Multi-Omics. In this paper, a long-short term memory based variational autoencoder (LSTM-VAE) is proposed for fault detection of maritime components onboard. [18] introduced a hybrid model of LSTM-VAE (LSTM Variational Autoencoder) to detect multimodal anoma-lies on robot-assisted feeding systems designed to help people with disabilities often needing physical assistance. LSTM-Based VAE-GAN for Time Series Anomaly Detection. in multivariate settings. The Long Short-Term Memory network or LSTM network […] Sep 25, 2019 · Here, we will use Long Short-Term Memory (LSTM) neural network cells in our autoencoder model. Lew, Andrew J and Buehler, Markus J. 3 stars 1 fork Branches Tags Activity. Oord et. 15 shows the illustration of the confirmation of diagnosis result function (i. These dependencies can be useful when you want the network Nov 17, 2021 · I am trying to implement LSTM based VAE. Input shape is (sample_number, 96, 24) And I want to have a model's output shape as (24) # encoder latent_dim = 24 inter_dim = 32 timesteps, features = 96, May 14, 2020 · We intentionally plot the reconstructed latent vectors using approximately the same range of values taken on by the actual latent vectors. Least Squares Generative Adversarial Network (LSGAN) ( Yasonik, 2020 ) is used to learn the corresponding transformation by minimizing the loss. We can see that the reconstructed latent vectors look like digits, and the kind of digit corresponds to the location of the latent vector in the latent space. All the models are trained on the CelebA dataset for consistency and comparison. This notebook demonstrates how to train a Variational Autoencoder (VAE) ( 1, 2) on the MNIST dataset. Mar 16, 2023 · LSTM-VAE: Long short-term memory-based variational autoencoder projects the inputs and their temporal dependency as latent space representations, thus estimating inputs expected distribution and Oct 5, 2021 · The t-SNE_VAE_bi-LSTM model is proposed in this study as a prediction model that combines the t-SNE, VAE, and bi-LSTM networks. However, the PCA does not work well in the abnormal data series Lstm variational auto-encoder for time series anomaly detection and features extraction Topics deep-learning time-series tensorflow vae anomaly-detection variational-autoencoder Dec 22, 2021 · A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. Cannot retrieve latest commit at this time. Classically, stochastic models that generate graphs with a pre-defined probability of edges and nodes have Mar 12, 2021 · The number of LSTM blocks was chosen from [24, 32, 64, 128, 256, 512] for one or two LSTM layers. main. 000, 0. Oct 9, 2019 · Moreover, the performance trend across the time series should be predicted. LSTM-VAE introduces regularization in the latent space using a probabilistic encoder and decoder. View. VAE in Pytorch: The VAE is realized in Pytorch 0. This work is the first attempt to integrate unsupervised anomaly detection and trend prediction under one Jun 26, 2021 · 段階的手法では、RNNを用いて各時点のデータの復元モデルを構築します。LSTM Encoder-Decoder, LSTM-VAE, GGM-VAE, OmniAnomalyなどが、この構造を取っています。しかし、弱点として正常データと異状のいずれにも過学習してしまう傾向があります。 Jun 1, 2023 · Firstly, a new CNN-VAE-based anomaly detection framework was proposed including a separate LSTM network to generate temporal-aware embeddings of the latent vector of the primary model. pytorch seq2seq model for poem/couplet generation 1 star 0 forks Branches Tags Activity. I'm not sure which part of my code being wrong, forgive me for posting all of them. Python 99. My import: The vae-lstm topic hasn't been used on any public repositories, yet. Dropout rates were chosen from 0. Fork 81. 1%. 2 to regularize all layers. , when Ab 01 event is diagnosed). 𝑧 The interesting results appear when we examine the top panels in Fig. 8710 than 5 other Jul 3, 2020 · In this paper, we propose a long short-term memory-based variational autoencoder generation adversarial networks (LSTM-based VAE-GAN) method for time series anomaly detection, which effectively solves the above problems. , 15 abnormal events and 1 normal state). This should give more wheight to the MSE and less to the KL divergence. The size of the latent space needs to be calibrated to capture generalizable patterns while avoiding noise and anomalies. wv wt fq fa mr gb zw xh fg gi