Substance or Style: What Does Your Image Embedding Know?
Cyrus Rashtchian Charles Herrmann Chun-Sung Ferng Ayan Chakrabarti Dilip Krishnan Deqing Sun Da-Cheng Juan Andrew Tomkins
Probes are small networks that predict properties of underlying data from
embeddings, and they provide a targeted, effective way to illuminate the
information contained in embeddings. While analysis through the use of probes
has become standard in NLP, there has been much less exploration in vision.
Image foundation models have primarily been evaluated for semantic content.
Better understanding the non-semantic information in popular embeddings (e.g.,
MAE, SimCLR, or CLIP) will shed new light both on the training algorithms and
on the uses for these foundation models. We design a systematic transformation
prediction task and measure the visual content of embeddings along many axes,
including image style, quality, and a range of natural and artificial
transformations. Surprisingly, six embeddings (including SimCLR) encode enough
non-semantic information to identify dozens of transformations. We also
consider a generalization task, where we group similar transformations and hold
out several for testing. We find that image-text models (CLIP and ALIGN) are
better at recognizing new examples of style transfer than masking-based models
(CAN and MAE). Overall, our results suggest that the choice of pre-training
algorithm impacts the types of information in the embedding, and certain models
are better than others for non-semantic downstream tasks.