Faster Depth-Adaptive Transformers.Yijin LiuFandong MengJie ZhouYufeng ChenJinan XuNational Conference on Artificial Intelligence
Introduced in:Depth-Adaptive Transformerby Elbayad et al. Current models perform a fixed number of computations for each input, regardless of the underlying complexity specific to each sequence. This problem was already highlighted in theUniversal Transformer, which proposes a repeated application of th...
Transformer encoder Transformers are natural text encoders and have had much success with language models recently14,34,41. As such, this motivated us to adapt the standard transformer to model assembly instructions. We developed and incorporated a transformer encoder, our adaptation of the MultiQuer...
(2019). The core idea of MobileNet lies in separating a normal convolution block into depth-wise convolution and point-wise convolution, which significantly reduces the mode parameters and computation time. Since the advent of ViT Dosovitskiy et al. (2021), numerous works have attempted to make...
Single-Head ViT;Faster Whisper;Transformer KF;Pick-and-Draw SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design Recently, efficient Vision Transformers have shown great perfo…
(不是我猜想的全图的depth概率。) 6. 实际用的loss 就是我们之前 uncertainty 常用的乘 exp(-var)的形式,如下图: 如果\tau 很大,证明 这个depth不准,我就少学一点 分子; 如果\tau 很小,证明这个depth准,我就多学一点 分子。 另外pytorch里的KL 一般是 一个 logsoftmax 和 一个 softmax 作为输入。大家...
Alpha-IoU improved the bounding box regression accuracy with adaptive reweighting. When α was between (0, 1). It reduced localization accuracy and produced poorly performing detection frames. α was closely related to the size of the loss function. He et al. [63] suggested α equal to 3. ...