Related papers: Label Assignment Distillation for Object Detection
Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for many tasks such as image and text classification, especially…
This paper has been withdrawn by the author due to a necessity of further editing. (Will be resubmitted here or elsewhere in editted form.)
Current state-of-the-art object detectors are at the expense of high computational costs and are hard to deploy to low-end devices. Knowledge distillation, which aims at training a smaller student network by transferring knowledge from a…
This paper has been withdrawn for extensive revision.
Previous knowledge distillation (KD) methods mostly focus on compressing network architectures, which is not thorough enough in deployment as some costs like transmission bandwidth and imaging equipment are related to the image size.…
This article has been removed by arXiv administrators due to falsified authorship.
Dataset distillation or condensation aims to condense a large-scale training dataset into a much smaller synthetic one such that the training performance of distilled and original sets on neural networks are similar. Although the number of…
Recent advances in deep learning has lead to rapid developments in the field of image retrieval. However, the best performing architectures incur significant computational cost. Recent approaches tackle this issue using knowledge…
This paper has been withdrawn
This article was withdrawn by the arXiv.org administrators since it plagiarizes math.AT/0401211.
Knowledge distillation constitutes a simple yet effective way to improve the performance of a compact student network by exploiting the knowledge of a more powerful teacher. Nevertheless, the knowledge distillation literature remains…
This paper has been withdrawn by the authors due to the violation of ATLAS experiment publication policy.
Multi-label classification is crucial for comprehensive image understanding, yet acquiring accurate annotations is challenging and costly. To address this, a recent study suggests exploiting unsupervised multi-label classification…
This article has been withdrawn by arXiv administrators due to plagiarized content from arXiv:1010.2469.
Dataset distillation (DD) aims to generate a compact yet informative dataset that achieves performance comparable to the original dataset, thereby reducing demands on storage and computational resources. Although diffusion models have made…
This work investigates the compatibility between label smoothing (LS) and knowledge distillation (KD). Contemporary findings addressing this thesis statement take dichotomous standpoints: Muller et al. (2019) and Shen et al. (2021b).…
In the vision domain, dataset distillation arises as a technique to condense a large dataset into a smaller synthetic one that exhibits a similar result in the training process. While image data presents an extensive literature of…
Admin note: withdrawn by arXiv admin because of the use of a pseudonym, in violation of arXiv policy.
This paper have been withdraw by the autors, because of a too early submission.
This paper has been withdrawn by the author(s), due a crucial error in the data.