In practice, however, such an assumption is extremely expensive even impossible to satisfy. Based on this observation, we reveal and study alatent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisylabels.

Neural Information Processing Systems http://nips.cc/

Moreover, we observe that the focus in the few related works falls on quantifying similarity in malware, often overlooking the clean data. This one-sided quantification is especially dangerous in the context of detection bypass.

Recently, Neural Architecture Search has achieved great success in large-scale imageclassification.

Effective attack approaches have been proposed for various domains such as imagerecognition[12],sentimentanalysis[25],andmalwaredetection[30].

One-shot pruning techniques offer a way to alleviate these burdens by removing redundant weights without the need for retraining. Y et, the massive scale of LLMs often forces current pruning approaches to rely on heuristics instead of optimization-based techniques, potentially resulting in suboptimal compression.