Interpreting the Second-Order Effects of Neurons in CLIP
<|reference_start|>Interpreting the Second-Order Effects of Neurons in CLIP: We interpret the function of individual neurons in CLIP by automatically describing them using text. Analyzing the direct effects (i.e. the flow from a neuron through the residual stream to the output) or the indirect effects (overall contribution) fails to capture the neurons' function in CLIP. Therefore, we present the "second-order lens", analyzing the effect flowing from a neuron through the later attention heads, directly to the output. We find that these effects are highly selective: for each neuron, the effect is significant for <2% of the images. Moreover, each effect can be approximated by a single direction in the text-image space of CLIP. We describe neurons by decomposing these directions into sparse sets of text representations. The sets reveal polysemantic behavior - each neuron corresponds to multiple, often unrelated, concepts (e.g. ships and cars). Exploiting this neuron polysemy, we mass-produce "semantic" adversarial examples by generating images with concepts spuriously correlated to the incorrect class. Additionally, we use the second-order effects for zero-shot segmentation and attribute discovery in images. Our results indicate that a scalable understanding of neurons can be used for model deception and for introducing new model capabilities.<|reference_end|>
@article{gandelsman2024interpreting,
    title={Interpreting the Second-Order Effects of Neurons in CLIP},
    author={Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt},
    journal={arXiv preprint arXiv:2406.04341},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04341},
    primaryClass={cs.CV}
}