1,CV界一直都是卷积,要把mask或者token等位置编码融入到卷积中有点难度,还好有了ViT; 2,信息密度问题(Information density)。与NLP不同的是图像信息存在很多冗余,例如一句话mask掉一两个单词,模型若想要补全这句话,需要非常high-level的理解,想想英语的完形填空。但是图像如果mask掉十几个相似点,对于图像的理解并不...
AE encoder-decoder架构,通过encoder将输入x编码成特征z,将z输入decoder进行解码得到x‘,因此,VE只能只能针对特定的… 阅读全文 赞同 2 添加评论 分享 收藏 DAG_GNN:一种基于VAE的DAG结构学习架构 Torea 北京邮电大学 电子科学与技术博士在读 ...
For example, in NLP, autoencoders can be used to learn word embeddings that obtain semantic similarities between words. These embeddings can also be used to improve text classification and sentiment analysis tasks.2. Anomaly DetectionThe ability of vanilla autoencoders to learn normal patterns in ...
We train multi-task (variational) autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders are attached, the better the models cluster sentences according ...
Decoder design.我们的MAE解码器可以灵活设计,如表1a和1b所示。 表1a改变了解码器深度(Transformer块的数量)。足够深的解码器对于线性探测很重要。这可以通过像素重建任务和识别任务之间的差距来解释:自编码器中的最后几层更专门用于重建,但与识别的相关性较小。一个合理深度的解码器可以解释重建的专门化,将潜在的表...
1. It has an encoder that maps an input to a latent representation and a decoder that reconstructs the input. e.g., PCA, K-means and DAE(denoising autoencoders: corrupt an input signal and learn to reconstruct the original, uncorrupted signal.) ...
decoder = Model(encoded_input, decoder_layer(encoded_input)) Then we need to compile the model with the ADAM optimizer and cross-entropy loss function fitment. autoencoder.compile(optimizer='adam', loss='binary_crossentropy') Then you need to load the data : ...
Code for 'LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders' - McGill-NLP/llm2vec
Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is only slowly adopting these models for text embedding tasks, which require rich contextualized representations. In this work, we introduce LLM2Vec, a...