Core Concepts
提案されたNoisy Layer Generation(NAYER)は、ランダムなノイズ入力からのサンプル生成における課題を解決し、高品質なサンプルを効率的に生成する方法を提供します。
Abstract
1. Abstract:
Existing approaches struggle to generate samples from random noise inputs.
Proposal of Noisy Layer Generation method (NAYER) relocates the random source to a noisy layer.
Utilizes meaningful constant label-text embedding (LTE) for high-quality sample generation.
2. Introduction:
Knowledge distillation aims to train a student model emulating a teacher model's capabilities.
Data-Free Knowledge Distillation (DFKD) transfers knowledge without accessing original data.
Challenges in generating diverse, high-quality samples addressed by NAYER.
3. Proposed Method:
Use of LTE as input accelerates training process and enhances sample quality.
Noisy Layer introduces randomness, preventing overemphasis on label information.
Generator and student networks trained jointly for effective knowledge transfer.
4. Experiments:
NAYER outperforms SOTA methods in accuracy and training time efficiency.
Speedup of 5 to 15 times achieved compared to previous approaches.
Superior performance demonstrated on CIFAR10, CIFAR100, TinyImageNet, and ImageNet datasets.
Stats
現在のSOTA DFKD手法はImageNetでのトレーニング時間が長いため結果を報告していない。
Quotes
"Almost state-of-the-art DFKD methods do not report results on large-scale ImageNet due to significant training time involved."
"NAYER achieves speeds that are 5 to even 15 times faster while also attaining higher accuracies compared to previous methods."