[ICCV2019] Detecting the Unexpected via Image Resynthesis

时间 2021-01-11

标签 ICCV2019 繁體版

原文原文链接

作者：Krzysztof Lis ， EPFL Introduction 利用GAN网络做图像中未知物体的 anomaly detection。该任务简单来说即为，训练数据中没有该类别物体以及其标签，但测试数据中有，如何定位该物体。传统思路有两种：1、根据预测的分割图中较低的confidence score来找未知物体的区域（segmentation uncertainty）；2、利用auto-e

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1. [ICCV2019] Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation
2. Few-shot Object Detection via Feature Reweighting (ICCV2019)
3. Detecting Deceptive Review Spam via Attention-Based Neural Networks
4. [ICCV2019] InGAN: Capturing and Remapping the “DNA” of a Natural Image
5. ICCV2019-ERL-Net：Entangled Representation Learning for Single Image De-Raining
6. CFSNet: Toward a Controllable Feature Space for Image Restoration ICCV2019
7. [ICCV2019] Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
8. Image Denoising via CNNs: An Adversarial Approach
9. 《GCAMatting：Natural Image Matting via Guided Contextual Attention》
10. The usage of docker image wurstmeister/kafka

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