Multiscale Attention Distillation for Object Detection

Neural Turing Machines have external memory that they can read and write to. Attentional Interfaces allow RNNs to focus on parts of their input.

A visual overview of neural attention, and the powerful extensions of neural networks being built on top of it. Distill is dedicated to clear explanations of ... Distill Hiatus · Distill Prize for Clarity in... · Distill Update 2018editoria

In this paper, we introduce the “Align-to-Distill” (A2D) strategy, designed to address the feature mapping problem by adaptively aligning student attention ...

We show that directly distilling information from the crucial attention mechanism from teacher to student can significantly narrow the performance gap between ...

In this paper, we introduce an effective and efficient feature distillation method utilizing all the feature levels of the teacher without manually selecting ...

Self-Attention Distilling 的基本思想. Self-attention distilling 通过一种近似或简化的方法来减少self-attention 机制的计算复杂度。具体来说，它通常 ...

在深度学习领域，注意力机制（Attention Mechanism）已经成为一种强大的工具，特别是在自然语言处理（NLP）和序列建模任务中。Using Fast Weights to Attend to ...

Knowledge distillation extracts general knowledge from a pre- trained teacher network and provides guidance to a target stu- dent network.

Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, ...

In this paper, we propose a Multiscale Attention-based Knowledge Distillation (MAD) method for object detection.