Papers › Quality-Agnostic Image Recognition via Invertible Decoder
Quality-Agnostic Image Recognition via Invertible Decoder
Insoo Kim, Seungju Han, Ji-won Baek, Seong-Jin Park, Jae-Joon Han, Jinwoo Shin
Despite the remarkable performance of deep models on image recognition tasks, they are known to be susceptible to common corruptions such as blur, noise, and low-resolution. Data augmentation is a conventional way to build a robust model by considering these common corruptions during the training. However, a naive data augmentation scheme may result in a non-specialized model for particular corruptions, as the model tends to learn the averaged distribution among corruptions. To mitigate the issue, we propose a new paradigm of training deep image recognition networks that produce clean-like features from any quality image via an invertible neural architecture. The proposed method consists of two stages. In the first stage, we train an invertible network with only clean images under the recognition objective. In the second stage, its inversion, i.e., the invertible decoder, is attached to a new recognition network and we train this encoder-decoder network using both clean and corrupted images by considering recognition and reconstruction objectives. Our two-stage scheme allows the network to produce clean-like and robust features from any quality images, by reconstructing their clean images via the invertible decoder. We demonstrate the effectiveness of our method on image classification and face recognition tasks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | ImageNet-C | QualNet (ResNeXt101) | mean Corruption Error (mCE) | 42.5 | #17 of 47 | Archive leaderboard | report |
| Domain Generalization | ImageNet-C | QualNet (ResNet-50) | mean Corruption Error (mCE) | 50.6 | #28 of 47 | Archive leaderboard | report |
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