虽然Transformer没有明确的循环形式,但本文证明通过以数据依赖的方式降低未归一化的注意力分数的权重,可以将遗忘门自然地融入到Transformer中。 (本文方法)将这种注意力机制命名为遗忘注意力(Forgetting Attention),并将由此产生的模型称为Forgetting Transformer(FoX) 本文研究表明,FoX在长上下文语言建模、长度外推和短...
1.提出了Restormer,是一种Encoder-Decoder的Transformer模型,能学习High-Resolution图像的Multi-Scale特征(体现在能处理各种各样尺度)与Local-Global特征(体现在MDTA中先使用了Dconv学习Local特征,再使用了Attention学习全局特征)。同时没有使用Windows Attention,没有使用切成patches,所以可以获取图像的遥远的上下文信息(expl...
Then, we give an overview structure of our efficient transformer network for visual tracking referred as ETT and describe the detail of TCA and MACA module. 3.1 Adaptive attention The multi-head attention in the original Transformer needs to learn the relevance between every two elements in the ...
可逆Transformer不需要在每一层中存储激活结果,在后面实验部分,我们对比使用了相同数量的参数,其表现与标准Transformer一样。 分块: 每一层Transformer中前馈网络所用的中间向量维度dff=4k甚至更高维度,依然非常占用内存;然而,一个序列中各个tokens在前馈网络层的计算是相互独立的,所以这部分计算可以拆分为c个组块: 这...
Restormer: Efficient Transformer for High-Resolution Image Restoration。 摘要 由于卷积神经网络(CNN)在从大规模数据中学习可推广的图像先验方面表现出色,这些模型已被广泛应用于图像复原及相关任务。近年来,另一类神经架构——Transformer,在自然语言和高级视觉任务上取得了显著的性能提升。虽然Transformer模型缓解了CNN的...
The Transformer decoder incorporates Spatially Modulated Co-Attention (SMCA) to preset the position of the target (human or object) in the image, narrow the search range of the query vector and accelerate the convergence of the model. In order to fuse multi-scale features and increase model ...
4.2 优化QKTQKT→ Memory-efficient attention QKTQKT→qiKTqiKT=> 对每个query单独计算,而无需计算整个大矩阵乘积 4.3 优化QKTQKT→ Shared-QK Transformer (Q = K) TransformerReformer (LSH attention) A→ linear projection 1 → Q A→ linear projection 2 → K ...
网易开源的针对transformer-based模型的推理加速框架,支持在中低端Ampere架构上单卡高性能推理百亿级模型。 项目背景 基于变压器的大规模模型已被证明在许多领域的各种任务中都是有效的。然而,将它们应用于工业生产需要繁重的工作来降低推理成本。为了填补这一空白,我们引入了一个可扩展的推理解决方案:Easy and Efficient ...
In this paper, we propose an efficient transformer architecture that uses reinforced positional embedding to obtain superior performance with half the number of encoder decoder layers. We demonstrate that concatenating positional encoding with trainable token embeddings, normalizing columns in the token embed...
However, extending Transformer to even larger context windows runs into limitations. The power of Transformer comes fromattention, the process by which it considers all possible pairs of words within the context window to understand the connections between them. So, in the case of a text of 100K...