Encoder-Decoder与Transformer ai2news.com/blog/33716/ 2021-10-29 DINO:Self-Supervised Vision Transformer的新特性 ai2news.com/blog/15991/ 2021-05-17 视觉架构大一统!港中文通过统一视角Container对Transformer、 深度卷积以及MLP-Mixer进行了大一统 ai2news.com/blog/18121/ 2021-06-04 用Transformer定义所有ML...
[247]: Self-supervised learning with swin transformers.arXiv preprint arXiv:2105.04553, 2021. [31]: Improved baselines with momentum contrastive learning.arXiv preprint arXiv:2003.04297, 2020. [88]: Momentum contrast for unsupervised visual representation learning. InCVPR, pages 9729–9738, 2020. 1...
V-C MULTILINGUAL DENOISING PRE-TRAINING FOR NEURAL MACHINE TRANSLATION: mBART V-C.1. Supervised Machine TranslationmBART表明,通过自回归预处理训练BART,通过从公共爬行(CC-25)语料库中对25种语言的去噪目标进行序列重构,与之前的技术相比,取得了相当大的性能增益[64]。mBART的参数微调可以是有监督的,也可以是...
我们将使用第二种安排,因此我们可以简单地将我们的构建模块连接在一起: class TransformerEncoderLayer(nn.Module): def __init__(self, config): super().__init__() self.layer_norm_1 = nn.LayerNorm(config.hidden_size) self.layer_norm_2 = nn.LayerNorm(config.hidden_size) self.attention = Mul...
【对于Unsupervised (self-supervised) pre-training任务】所采用的方法是将多维时序数据的的部分段设置为 0(文中称为masked sequences) 并通过其他非0段的序列预测masked sequences,用此方式构建自监督学习策略。需要控制masked sequences的长度,太短的序列太容易预测,没有意义。具体过程是在每个时间步,平均r\cdot{m...
Traditional image classification techniques can be broadly categorized into supervised and unsupervised methods28. Supervised classification methods, such as maximum likelihood classification (MLC) and support vector machine (SVM), rely on labeled training data to create a model that can predict the class...
[MemMC-MAE] Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder [paper] [code] Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection [paper] [code] [VideoMAE] VideoMAE: Masked Autoencode...
It incorporates the sequence embedding from a supervised transformer protein language model into a multi-scale network enhanced by knowledge distillation to predict inter-residue two-dimensional geometry, which is then used to reconstruct three-dimensional structures via energy minimization. Benchmark tests...
2.2 Unsupervised Fine-tuning Approaches 2.3 Transfer Learning from Supervised Data 3 BERT 3.1 Pre-training BERT 3.2 Fine-tuning BERT 4 Experiments 4.1 GLUE 4.2 SQuAD v1.1 4.3 SQuAD v2.0 4.4 SWAG 5 Ablation Studies 5.1 Effect of Pre-training Tasks ...
Pre-training:Unsupervised pre-training(LM) Fine-tuning:Supervised fine-tuning(Classification、Entailment、Similarity、Multiple Choice、Question Answering) GPT2:(单向通用模型Byte Pair Encoding,BPE) For example, a translation training example can be written as the sequence(translate to french, english text...