Related papers: Label Assignment Distillation for Object Detection
Recently, a variety of regularization techniques have been widely applied in deep neural networks, such as dropout, batch normalization, data augmentation, and so on. These methods mainly focus on the regularization of weight parameters to…
This paper has been withdrawn by the authors.
Knowledge distillation involves transferring the predictive capabilities of large, high-performing AI models (teachers) to smaller models (students) that can operate in environments with limited computing power. In this paper, we address…
Erroneous submission in violation of copyright, removed by arXiv admin.
This submission has been withdrawn by arXiv administrators because of inappropriate authorship claims.
This paper was withdrawn by arXiv administrators. It is an erroneous duplicate submission of math.NA/0405095.
Erroneous submission in violation of copyright removed by arXiv admin.
This paper has been withdrawn by the author due to a crucial error.
Knowledge Distillation (KD) utilizes training data as a transfer set to transfer knowledge from a complex network (Teacher) to a smaller network (Student). Several works have recently identified many scenarios where the training data may…
This paper was removed by arXiv admin due to 94% plagiarism from uncited reference hep-th/0507153.
Knowledge distillation allows transferring knowledge from a pre-trained model to another. However, it suffers from limitations, and constraints related to the two models need to be architecturally similar. Knowledge distillation addresses…
This paper has been withdrawn by the authors
This paper has been administratively withdrawn by arXiv, duplicate of arXiv:1008.2691.
Withdrawn by arXiv administrators due to content entirely plagiarized from other authors (not in arXiv).
This paper has been withdrawn by the authors. Because of a misunderstanding, the paper was submitted prematurely to the arXiv. A replacement will follow.
Large-scale image generation models, with impressive quality made possible by the vast amount of data available on the Internet, raise social concerns that these models may generate harmful or copyrighted content. The biases and harmfulness…
Knowledge distillation from proprietary LLM APIs poses a growing threat to model providers, yet defenses against this attack remain fragmented and unevaluated. We present DistillGuard, a framework for systematically evaluating output-level…
This submission has been withdrawn by arXiv administration.
Optimizing neural networks with noisy labels is a challenging task, especially if the label set contains real-world noise. Networks tend to generalize to reasonable patterns in the early training stages and overfit to specific details of…
This article has been withdrawn.