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attention-transfer - Improving Convolutional Networks via Attention Transfer (ICLR 2017)

  •    Jupyter

The code uses PyTorch https://pytorch.org. Note that the original experiments were done using torch-autograd, we have so far validated that CIFAR-10 experiments are exactly reproducible in PyTorch, and are in process of doing so for ImageNet (results are very slightly worse in PyTorch, due to hyperparameters). This section describes how to get the results in the table 1 of the paper.

DICOD - Official Pytorch implementation for Distilling Image Classifiers in Object detection

  •    Python

Code is in early release and may be subject to change. Please feel free to open an issue in case of questions. We use PyTorch and MMDetection v2.10.0 as the codebase.

DIODE - Official PyTorch implementation of Data-free Knowledge Distillation for Object Detection, WACV 2021

  •    Jupyter

This repository is the official PyTorch implementation of Data-free Knowledge Distillation for Object Detection, WACV 2021. Abstract: We present DeepInversion for Object Detection (DIODE) to enable data-free knowledge distillation for neural networks trained on the object detection task. From a data-free perspective, DIODE synthesizes images given only an off-the-shelf pre-trained detection network and without any prior domain knowledge, generator network, or pre-computed activations. DIODE relies on two key components—first, an extensive set of differentiable augmentations to improve image fidelity and distillation effectiveness. Second, a novel automated bounding box and category sampling scheme for image synthesis enabling generating a large number of images with a diverse set of spatial and category objects. The resulting images enable data-free knowledge distillation from a teacher to a student detector, initialized from scratch. In an extensive set of experiments, we demonstrate that DIODE’s ability to match the original training distribution consistently enables more effective knowledge distillation than out-of-distribution proxy datasets, which unavoidably occur in a data-free setup given the absence of the original domain knowledge.









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