Understanding invariance via feedforward inversion of discriminatively trained classifiers

By Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer. 2021

11 citations recorded by the source corpus.

A discriminatively trained neural net classifier can fit the training data perfectly if all information about its input other than class membership has been discarded prior to the output layer. Surprisingly, past research has discovered that some extraneous visual detail remains in the logit vector. This finding is based on inversion techniques that map deep embeddings back to images. We explore this phenomenon further using a novel synthesis of methods, yielding a feedforward inversion model that produces remarkably high fidelity reconstructions, qualitatively superior to those of past efforts. When applied to an adversarially robust classifier model, the reconstructions contain sufficient local detail and global structure that they might be confused with the original image in a quick glance, and the object category can clearly be gleaned from the reconstruction. Our approach is based on BigGAN (Brock, 2019), with conditioning on logits instead of one-hot class labels. We use our reconstruction model as a tool for exploring the nature of representations, including: the influence of model architecture and training objectives (specifically robust losses), the forms of invariance that networks achieve, representational differences between correctly and incorrectly classified images, and the effects of manipulating logits and images. We believe that our method can inspire future investigations into the nature of information flow in a neural net and can provide diagnostics for improving discriminative models.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Deduplicating Training Data Makes Language Models Better (2021, methodology): “In computer vision, memorization of training data has been studied from various angles for both discriminative and generative models (e.g. Arpit et al., 2017; Webster et al., 2019; Feldman and Zhang, 2020; Stephenson et al., 2021; Teterwak et al., 2021).”
Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion (2021, methodology): “Naively combining BN-guided data synthesis and other existing inversion approaches is suboptimal due to the following reasons: • Prior work [36, 54] train a generator (in a GAN framework) as the inversion model, whereas the GAN training is very sensitive and challenging given only synthetic data [72”