Classifying corrupted images
Our robust encoders observe highly corrupted images and use a simple linear probe to classify. We present the top 3 classes from our models as well as those from the end-to-end supervised baselines. For three different types of forward operators, our robust encoders classify correctly and also produce reasonable top 3 alternatives. On the contrary, the supervised baselines completely fail even though they were fine-tuned on exactly this task to classify corrupted images, starting from a powerful ImageNet pretrained ResNet-101. We also expect that most humans would fail to classify such highly corrupted images.
We study a new family of inverse problems for recovering representations of corrupted data. We assume access to a pre-trained representation learning network R(x) that operates on clean images, like CLIP. The problem is to recover the representation of an image R(x), if we are only given a corrupted version A(x), for some known forward operator A. We propose a supervised inversion method that uses a contrastive objective to obtain excellent representations for highly corrupted images. Using a linear probe on our robust representations, we achieve a higher accuracy than end-to-end supervised baselines when classifying images with various types of distortions, including blurring, additive noise, and random pixel masking. We evaluate on a subset of ImageNet and observe that our method is robust to varying levels of distortion. Our method outperforms end-to-end baselines even with a fraction of the labeled data in a wide range of forward operators.
Our proposed method
We initialize a student and teacher model from a pretrained CLIP encoder. Clean image batches are fed to the teacher while distorted versions of those images are fed to the student. The student is trained using a contrastive loss which makes student and teacher representations of the same original images more similar while making their representations of different images less similar.
Ravula, S., Smyrnis, G., Jordan, M., & Dimakis, A. G. (2021). Inverse Problems Leveraging Pre-trained Contrastive Representations. Advances in Neural Information Processing Systems, 34, 8753-8765.