In this section, we conduct an ablation study analyzing the effects of iterations in our proposed approach. Moreover, we see that as the iterations progress, the 2D landmark prediction error continues to reduce as seen in Figure 1a. The red points represent the frames that were given initial 2D input labels. The colorbar of these scatter plots represents the reprojection error (Eq. Weassume that only asingle object of interest (Chimpanzee in Figure 1is visible in each frame.

Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models.

Large language models are usually fine-tuned to align with human preferences. However, fine-tuning a large language model can be challenging. In this work, we introduceweak-to-strong search, framing the alignment of a large language model as a test-time greedy search to maximize the log-probability difference between small tuned and untuned models while sampling from the frozen large model. This method serves both as (1) a compute-efficient model up-scaling strategy that avoids directly tuning the large model and as (2) an instance of weak-to-strong generalization thatenhances astrong model with weak test-time guidance.

The mixing coefficients are indirectly computed through querying a separate corresponding semantic memory using the data pair. We demonstrate empirically thatFINEcanstrongly improveout-of-distribution generalization on IQ tasks that involve geometric transformations.