Transformer has been widely used for self-supervised pre-training in Natural Language Processing (NLP) and achieved great success. However, it has not been fully explored in visual self-supervised learning. Meanwhile, previous methods only consider the high-level feature and learning representation fro...
A sufficiently deep decoder is important for linear probing. This can be explained by the gap between a pixel reconstruction task and a recognition task: the last several layers in an autoencoder are more specialized for reconstruction, but are less relevant for recognition. 不同的任务,模型选择的...
This can be explained by the gap be- tween a pixel reconstruction task and a recognition task: the last several layers in an autoencoder are more specialized for reconstruction, but are less relevant for recognition. A reasonably deep decoder can account for the reconstruction specialization, ...
Our method is based on the observation that the temporal stochastic evolution manifests itself in local patterns. We show that we can exploit these patterns to infer the underlying graph by formulating a masked reconstruction task. Therefore, we proposeGINA(GraphInferenceNetworkArchitecture), a machine...
Masked language modeling in BERT The BERT model is an example of a pretrained MLM that consists of multiple layers of transformer encoders stacked on top of each other. Various large language models, such as BERT, use a fill-in-the-blank approach in which the model uses the context words ...
This approach can be better explained by looking at it through the lens of energy-based models (EBMs) [34]. In the context of self-supervised learning, the primary goal is to assign higher energy levels to inputs that are dissimilar in semantics, while assigning lower energy levels to ...
Masked language modeling and its autoregressive counteroarts (BERT and GPT) 1. 提供一部分的输入序列,训练模型来预测另一部分序列(缺失)的内容。---这些方法已经被证明可以很好地扩展[4],大量的证据表明,这些预先训练的表征可以很好地推广到各种下游任务。