Feb 19, 2025 · From their early days as a dimensionality reduction technique to modern incarnations fueling generative art or anomaly detection, autoencoders remain a cornerstone of unsupervised (and...

Jan 18, 2020 · Summary: Unsupervised Learning deals with data without labels. The problem we wish to tackle with auto-encoders is: Given that the data that has many features, can we construct a smaller set...

Mar 19, 2023 · The encoder converts input data into a latent representation, while the decoder produces output data from the latent representation. In this blog post, we will look at autoencoder...

Apr 9, 2024 · We’re excited to present LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders, a simple and efficient solution to transform any decoder-only LLM into a powerful text encoder in an unsupervised fashion simply by using adapters (LoRA), without the need to modify the base models.

Sep 21, 2020 · Autoencoders are a creative application of deep learning to unsupervised problems; an important answer to the quickly growing amount of unlabeled data. Autoencoders are characterized by an input the same size as the output and an architectural bottleneck.

In existing methods for hyperspectral and multispectral fusion, correlation between spectral and spatial domains in HSIs is mostly neglected. To address this issue, an unsupervised encoder–decoder network under spatial and spectral guidance for the HSI–MSI fusion (uEDSSG) was proposed in this study.

In this project, we employ an unsupervised process grounded in pre-trained Transformers-based Sequential Denoising Auto-Encoder (TSDAE), introduced by the Ubiquitous Knowledge Processing Lab of Darmstadt, which can realize a performance level reaching 93.1% of in-domain supervised methodologies.

Mar 21, 2022 · While we often use Neural Networks in a supervised manner with labelled training data, we can also use them in an unsupervised or self-supervised way, e.g., by employing Autoencoders. Hence, I felt that the universality of Neural Networks and their unique approach to Machine Learning deserved a separate category.

Oct 1, 2020 · This paper proposes a Self-Taught Encoder-Decoder Network (STED-Net), which consists of a representation sub-network and a classification sub-network, for unsupervised feature learning. On one hand, the representation sub-network maps images to …

Sep 6, 2021 · In this paper, we propose encoder-deep convolutional generative adversarial network (EGAN) as a solution for unsupervised feature learning. In EGAN, an encoder is put on top of GAN’s Generator networks to avoid GAN learns …