Thesebiased models suffer from the poor generalization capability when evaluated on unbiased datasets. Existing approaches for debiasing often identify and emphasize those samples withnosuchcorrelation (i.e.,bias-conflicting)without defining the bias type in advance. However, such bias-conflicting samples are significantly scarce in biased datasets, limiting the debiasing capability of these approaches.

Weprovetheir equivalence to the kernel regression predictor with the associated NTK, which expands the application scope of NTK. Based on our results, we elucidate the separation ofPNNs overstandard neural networks with respect toextrapolation andspectralbias.

Weprovetheir equivalence to the kernel regression predictor with the associated NTK, which expands the application scope of NTK. Based on our results, we elucidate the separation ofPNNs overstandard neural networks with respect toextrapolation andspectralbias.

While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored.

The code is availableat tt s t s q.

Indoing so, we generalize existing approaches and offer information-theoretic corrections and efficient variational approximations.