BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models

Disentangled Inference for GANs with Latently Invertible Autoencoder

Generative Adversarial Networks (GANs) play an increasingly important role in machine learning. However, there is one fundamental issue hindering their practical applications: the absence of capability for encoding real-world samples. The conventional way of addressing this issue is to learn an encoder for GAN via Variational Auto-Encoder (VAE). In this paper, we show that the entanglement of the latent space for the VAE/GAN framework poses the main challenge for encoder learning. To address the entanglement issue and enable inference in GAN we propose a novel algorithm named Latently Invertib…

Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification

Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets. In this model, we develop a GAN architecture with an additional autoencoder component, where recurrent neural networks (RNNs) are used for each component of the model in order to generate synthetic data to improve classification accuracy for a highly imbalanced medical device dataset. In addition to the medical device dataset, …

Implicit Discriminator in Variational Autoencoder

Recently generative models have focused on combining the advantages of variational autoencoders (VAE) and generative adversarial networks (GAN) for good reconstruction and generative abilities. In this work we introduce a novel hybrid architecture, Implicit Discriminator in Variational Autoencoder (IDVAE), that combines a VAE and a GAN, which does not need an explicit discriminator network. The fundamental premise of the IDVAE architecture is that the encoder of a VAE and the discriminator of a GAN utilize common features and therefore can be trained as a shared network, while the decoder of t…

基于自编码器-CNN-GANs算法的加密货币交易策略开发

Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms

本文提出一种结合去噪自编码器、一维CNN和GANs的加密货币交易策略，通过过滤降噪、特征提取与生成对抗建模，预测大幅价格波动的可能性和方向，实证验证了机器学习在金融序列模式发现中的有效性。

Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets

The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning.While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance.Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to trai…

Effect of backdoor attacks over the complexity of the latent space distribution

The input space complexity determines the model's capabilities to extract their knowledge and translate the space of attributes into a function which is assumed in general, as a concatenation of non-linear functions between layers. In the presence of backdoor attacks, the space complexity changes, and induces similarities between classes that directly affect the model's training. As a consequence, the model tends to overfit the input set. In this research, we suggest the D-vine Copula Auto-Encoder (VCAE) as a tool to estimate the latent space distribution under the presence of backdoor trigger…

生成式人工智能的进展：对GAN、GPT、自编码器、扩散模型和Transformer的综合综述

Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers

本文系统综述了以ChatGPT为代表的生成式AI进展，涵盖GAN、GPT、自编码器、扩散模型和Transformer等核心模型，分析其多样化任务能力、面临的挑战及未来前景。

Controllable Level Blending between Games using Variational Autoencoders

Previous work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs are artificial neural networks that learn and use latent representations of datasets to generate novel outputs. We train a VAE on level data from Super Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning both games. We then use this space to generate level segments that combine properties of levels from both games. Moreover, by applying evolu…

Molecular Generative Model Based On Adversarially Regularized Autoencoder

Deep generative models are attracting great attention as a new promising approach for molecular design. All models reported so far are based on either variational autoencoder (VAE) or generative adversarial network (GAN). Here we propose a new type model based on an adversarially regularized autoencoder (ARAE). It basically uses latent variables like VAE, but the distribution of the latent variables is obtained by adversarial training like in GAN. The latter is intended to avoid both inappropriate approximation of posterior distribution in VAE and difficulty in handling discrete variables in G…

AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

Non-parallel many-to-many voice conversion, as well as zero-shot voice conversion, remain under-explored areas. Deep style transfer algorithms, such as generative adversarial networks (GAN) and conditional variational autoencoder (CVAE), are being applied as new solutions in this field. However, GAN training is sophisticated and difficult, and there is no strong evidence that its generated speech is of good perceptual quality. On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN. In this paper, we propose a new style transfer scheme that…

Pioneer Networks: Progressively Growing Generative Autoencoder

We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial networks (GANs) are known for their ability to simulate random high-quality images, but they cannot reconstruct existing images. Previous works have attempted to extend GANs to support such inference but, so far, have not delivered satisfactory high-quality results. Instead, we propose the Progressively Growing Generative Autoencoder (PIONEER) network which achiev…

Simplex Autoencoders

Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces. In this work, we propose a new approach that models the latent space of an Autoencoder as a simplex, allowing for a novel heuristic for determining the number of components in the mixture model. This heuristic is independent of the number of classes and produces comparable results. We also introduce a sampling m…

VagueGAN：针对图像生成流水线的隐蔽投毒与后门攻击

VAGUEGAN: Stealthy Poisoning and Backdoor Attacks on Image Generative Pipelines

本文提出VagueGAN攻击流水线，利用PoisonerNet与生成器-判别器对制作隐蔽触发器，并验证其可迁移至ControlNet扩散编辑流程。

An Empirical Study of Generative Models with Encoders

Generative adversarial networks (GANs) are capable of producing high quality image samples. However, unlike variational autoencoders (VAEs), GANs lack encoders that provide the inverse mapping for the generators, i.e., encode images back to the latent space. In this work, we consider adversarially learned generative models that also have encoders. We evaluate models based on their ability to produce high quality samples and reconstructions of real images. Our main contributions are twofold: First, we find that the baseline Bidirectional GAN (BiGAN) can be improved upon with the addition of an …

Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VA…

Detect and remove watermark in deep neural networks via generative adversarial networks

Deep neural networks (DNN) have achieved remarkable performance in various fields. However, training a DNN model from scratch requires a lot of computing resources and training data. It is difficult for most individual users to obtain such computing resources and training data. Model copyright infringement is an emerging problem in recent years. For instance, pre-trained models may be stolen or abuse by illegal users without the authorization of the model owner. Recently, many works on protecting the intellectual property of DNN models have been proposed. In these works, embedding watermarks i…

PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification

We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech applications. The AAE architecture allows us to obtain good synthetic speech leveraging upon a discriminative training of latent vectors. Such synthetic speech is used to build a privacy-preserving classifier when non-sensitive data is not sufficiently available in the public domain. This classifier follows the PATE scheme that uses an ensemble of noisy outputs to label the synthetic samp…

Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation

Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling policy directly in a discrete action space. Such methods compute the rewards from complete sentences and avoid error accumulation due to exposure bias. Other approaches employ approximation techniques that map the text to continuous representation in order to circumvent the non-differentiable discrete process. Particularly, autoencoder-based methods eff…

Doubly Stochastic Adversarial Autoencoder

Any autoencoder network can be turned into a generative model by imposing an arbitrary prior distribution on its hidden code vector. Variational Autoencoder (VAE) [2] uses a KL divergence penalty to impose the prior, whereas Adversarial Autoencoder (AAE) [1] uses {\it generative adversarial networks} GAN [3]. GAN trades the complexities of {\it sampling} algorithms with the complexities of {\it searching} Nash equilibrium in minimax games. Such minimax architectures get trained with the help of data examples and gradients flowing through a generator and an adversary. A straightforward modifica…

Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

Data is the fuel of data science and machine learning techniques for smart grid applications, similar to many other fields. However, the availability of data can be an issue due to privacy concerns, data size, data quality, and so on. To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase.We compared the Kull…

GANcrop：一种针对联邦学习中后门攻击的对比防御方法

GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning

本文提出针对联邦学习后门攻击的防御机制GANcrop，利用对比学习识别恶意模型，并结合生成对抗网络恢复触发器实施缓解，有效防御攻击且保持模型准确率。

一种贝叶斯非参数生成模型方法：利用Wasserstein距离和最大均值差异整合变分自编码器与生成对抗网络

A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy

本文提出一种基于贝叶斯非参数框架的生成模型，整合VAE、GAN及CGAN，采用Wasserstein距离和最大均值差异损失，以解决过拟合和噪声样本问题，提升训练稳定性并生成高质量多样样本。

Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks

Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the generative adversarial networks (GANs) leans to majority class samples. The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this problem and restore the balance to the data. The former pre-trains the autoencoder weights in an unsupervised manner. However, it is unstable when the images from different categories have similar features. The latter is …

Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders

Generative models and inferential autoencoders mostly make use of $\ell_2$ norm in their optimization objectives. In order to generate perceptually better images, this short paper theoretically discusses how to use Structural Similarity Index (SSIM) in generative models and inferential autoencoders. We first review SSIM, SSIM distance metrics, and SSIM kernel. We show that the SSIM kernel is a universal kernel and thus can be used in unconditional and conditional generated moment matching networks. Then, we explain how to use SSIM distance in variational and adversarial autoencoders and uncond…

Multi-Adversarial Variational Autoencoder Networks

The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform image-based unsupervised clustering or semi-supervised classification. Combining the power of these two generative models, we introduce Multi-Adversarial Variational autoEncoder Networks (MAVENs), a novel network architecture that incorporates an ensemble of discriminators in a VAE-GAN network, with simultaneous adversarial learning and variational inference. We apply MAVENs to the generation of synthetic images and propose a new distribution measure to quanti…

Generative Adversarial Stacked Autoencoders

Generative Adversarial Networks (GANs) have become predominant in image generation tasks. Their success is attributed to the training regime which employs two models: a generator G and discriminator D that compete in a minimax zero sum game. Nonetheless, GANs are difficult to train due to their sensitivity to hyperparameter and parameter initialisation, which often leads to vanishing gradients, non-convergence, or mode collapse, where the generator is unable to create samples with different variations. In this work, we propose a novel Generative Adversarial Stacked Convolutional Autoencoder(GA…

Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency

The introduction of Variational Autoencoders (VAE) has been marked as a breakthrough in the history of representation learning models. Besides having several accolades of its own, VAE has successfully flagged off a series of inventions in the form of its immediate successors. Wasserstein Autoencoder (WAE), being an heir to that realm carries with it all of the goodness and heightened generative promises, matching even the generative adversarial networks (GANs). Needless to say, recent years have witnessed a remarkable resurgence in statistical analyses of the GANs. Similar examinations for Aut…

经典GAN的指数容量扩展与混合潜在风格量子GAN的比较

Exponential capacity scaling of classical GANs compared to hybrid latent style-based quantum GANs

本文研究了混合潜在风格量子GAN在图像生成中的指数容量优势，发现经典GAN与量子GAN在参数规模上呈指数级增长关系。

使用变分自编码器增强人机交互的动态用户界面生成

Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders

本研究提出基于变分自编码器（VAE）的动态用户界面生成与优化方法，利用RICO数据集训练并整合实时用户行为数据，实验证明其生成质量与精度优于AE、GAN等基线方法。

MAD-VAE: Manifold Awareness Defense Variational Autoencoder

Although deep generative models such as Defense-GAN and Defense-VAE have made significant progress in terms of adversarial defenses of image classification neural networks, several methods have been found to circumvent these defenses. Based on Defense-VAE, in our research we introduce several methods to improve the robustness of defense models. The methods introduced in this paper are straight forward yet show promise over the vanilla Defense-VAE. With extensive experiments on MNIST data set, we have demonstrated the effectiveness of our algorithms against different attacks. Our experiments al…

IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis

We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way. On one hand, the generator is required to reconstruct the input images from the noisy outputs of the inference model as normal VAEs. On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs.…

GAN 的对偶空间训练：通往高效且富有创造力的生成模型之路

Dual Space Training for GANs: A Pathway to Efficient and Creative Generative Models

针对 GAN 训练资源消耗大的问题，提出利用 autoencoder 可逆映射在数据对偶空间的编码表示上训练 GAN，以提升效率并探索超越人类识别的生成模式。

一种利用自编码器与生成对抗网络协同合成翼型空气动力学计算流体力学结果的框架

A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics

本文提出将自编码器与条件GAN协同用于翼型CFD结果生成，以20维向量编码几何并生成压力分布图，从而降低气动预测成本、提升翼型设计与优化效率。

Megapixel Image Generation with Step-Unrolled Denoising Autoencoders

An ongoing trend in generative modelling research has been to push sample resolutions higher whilst simultaneously reducing computational requirements for training and sampling. We aim to push this trend further via the combination of techniques - each component representing the current pinnacle of efficiency in their respective areas. These include vector-quantized GAN (VQ-GAN), a vector-quantization (VQ) model capable of high levels of lossy - but perceptually insignificant - compression; hourglass transformers, a highly scaleable self-attention model; and step-unrolled denoising autoencoder…

SeriesGAN：通过对抗和自回归学习生成时间序列

SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning

针对 GAN 时间序列生成的收敛与嵌入信息损失问题，提出结合自动编码器、双判别器和 teacher-forcing 监督的 SeriesGAN，显著提升生成保真度并超越现有基准。

CASS: Cross Adversarial Source Separation via Autoencoder

This paper introduces a cross adversarial source separation (CASS) framework via autoencoder, a new model that aims at separating an input signal consisting of a mixture of multiple components into individual components defined via adversarial learning and autoencoder fitting. CASS unifies popular generative networks like auto-encoders (AEs) and generative adversarial networks (GANs) in a single framework. The basic building block that filters the input signal and reconstructs the $i$-th target component is a pair of deep neural networks $\mathcal{EN}_i$ and $\mathcal{DE}_i$ as an encoder for …

Variational Diffusion Autoencoders with Random Walk Sampling

Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to approximate a homeomorphism between the data distribution and the sampling space. This is a construction that strives to define the data manifold. A major obstacle to VAEs and GANs, however, is choosing a suitable prior that matches the data topology. Well-known consequences of poorly picked priors are posterior and mode collapse. To our knowledge, no existing method sidesteps this user choice. Conversely, $\textit{diff…

Adversarial Autoencoders

In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. Matching the aggregated posterior to the prior ensures that generating from any part of prior space results in meaningful samples. As a result, the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution. We sh…

Disentangled Representation Learning Using ($β$-)VAE and GAN

Given a dataset of images containing different objects with different features such as shape, size, rotation, and x-y position; and a Variational Autoencoder (VAE); creating a disentangled encoding of these features in the hidden space vector of the VAE was the task of interest in this paper. The dSprite dataset provided the desired features for the required experiments in this research. After training the VAE combined with a Generative Adversarial Network (GAN), each dimension of the hidden vector was disrupted to explore the disentanglement in each dimension. Note that the GAN was used to im…

探索视觉问答中的多样化方法

Exploring Diverse Methods in Visual Question Answering

本研究基于平衡数据集，对比了GAN、自编码器和结合MCB的注意力机制在VQA任务中的表现，揭示了各方法的优劣与权衡，为未来研究指明方向。

MIDI-Sandwich: Multi-model Multi-task Hierarchical Conditional VAE-GAN networks for Symbolic Single-track Music Generation

Most existing neural network models for music generation explore how to generate music bars, then directly splice the music bars into a song. However, these methods do not explore the relationship between the bars, and the connected song as a whole has no musical form structure and sense of musical direction. To address this issue, we propose a Multi-model Multi-task Hierarchical Conditional VAE-GAN (Variational Autoencoder-Generative adversarial networks) networks, named MIDI-Sandwich, which combines musical knowledge, such as musical form, tonic, and melodic motion. The MIDI-Sandwich has two…

PixelGAN Autoencoders

In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code. We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder. For example, by imposing a Gaussian distribution as the prior, we can achieve a global vs. local decomposition, or by imposing …

on the effectiveness of generative adversarial network on anomaly detection

Identifying anomalies refers to detecting samples that do not resemble the training data distribution. Many generative models have been used to find anomalies, and among them, generative adversarial network (GAN)-based approaches are currently very popular. GANs mainly rely on the rich contextual information of these models to identify the actual training distribution. Following this analogy, we suggested a new unsupervised model based on GANs --a combination of an autoencoder and a GAN. Further, a new scoring function was introduced to target anomalies where a linear combination of the intern…

使用弱监督训练和权重选择的GAN学习与生成多样化住宅负荷模式

Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection

针对住宅负荷数据稀缺问题，提出RLP-GAN弱监督GAN框架，结合过完备自编码器和权重选择生成多样负荷模式，在417户真实数据上优于现有模型，并发布百万级合成数据集。

Topic Modeling with Wasserstein Autoencoders

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training …

Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "priors" at different resolution scale to improve the compression efficiency, and also employs the generative adversarial network(GAN) with multiscale discriminators to perform the end-to-end trainable rate-distortion optimization. We compare the perceptual quality of our reconstructions with traditional compression algorithms using High-Efficiency Video Coding(HEVC) based Intra Profile and JPEG2000 on the public Cityscape…

利用深度VAE-GAN增强数据同化中储层属性的参数化

Enhancing the Parameterization of Reservoir Properties for Data Assimilation Using Deep VAE-GAN

提出将VAE-GAN与ESMDA结合，以同时实现高质量储层描述和良好历史拟合，克服传统方法在非高斯分布和有限集合大小上的局限。

基于去噪自编码器的深度神经网络不可感知样本特定后门

Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder

提出基于去噪自编码器的不可感知样本特定后门攻击，触发器因样本而异且不可见，在ImageNet和MS-Celeb-1M上实现近100%攻击成功率且对干净数据影响极小，并能有效规避主流后门防御。

GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANs

While generative adversarial networks (GAN) are popular for their higher sample quality as opposed to other generative models like the variational autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty of the evaluation of generated samples. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among classes), the use of disentangled latent spaces, agreement of said evaluation metric with human perception, etc. In this paper, we propose a new score, namely, GM Score, which takes into various factors…

联合随机逼近自编码器及其在半监督学习中的应用

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning

针对现有深度生成模型处理离散数据及优化准则与似然间接相关的问题，提出联合随机逼近自编码器，直接最大化对数似然并最小化包含式KL散度，在半监督任务中性能媲美最先进连续潜变量模型。

Deep Automodulators

We introduce a new category of generative autoencoders called automodulators. These networks can faithfully reproduce individual real-world input images like regular autoencoders, but also generate a fused sample from an arbitrary combination of several such images, allowing instantaneous 'style-mixing' and other new applications. An automodulator decouples the data flow of decoder operations from statistical properties thereof and uses the latent vector to modulate the former by the latter, with a principled approach for mutual disentanglement of decoder layers. Prior work has explored simila…

Lookahead optimizer improves the performance of Convolutional Autoencoders for reconstruction of natural images

Autoencoders are a class of artificial neural networks which have gained a lot of attention in the recent past. Using the encoder block of an autoencoder the input image can be compressed into a meaningful representation. Then a decoder is employed to reconstruct the compressed representation back to a version which looks like the input image. It has plenty of applications in the field of data compression and denoising. Another version of Autoencoders (AE) exist, called Variational AE (VAE) which acts as a generative model like GAN. Recently, an optimizer was introduced which is known as looka…

采样无法压缩的内容

Sample what you cant compress

本研究提出SWYCC方法，将自编码器与扩散模型结合，在扩散损失下联合学习编码解码器，实现更高压缩率和更好重建质量，且表示更易建模。

后门稀疏自编码器

Backdooring Sparse Autoencoders

本研究揭示稀疏自编码器（SAE）可被植入后门，在不改动语言模型的情况下诱导特定行为，证明SAE是安全敏感组件，需防范供应链攻击。