Fast Training of Diffusion Models with Masked Transformers
Thống kê
50% masking ratio is used during training.
FID of 5.69 achieved on ImageNet-256×256 without guidance.
FID of 2.28 achieved on ImageNet-512×512 with guidance.
Total training cost is 273 hours on 8× A100 GPUs for ImageNet-256×256.
Total training cost is 209 A100 GPU days for ImageNet-512×512.
Trích dẫn
"Masked transformers reduce the training cost significantly."
"Our method achieves competitive performance with reduced computational resources."
"Efficiently train large transformer-based diffusion models without sacrificing generative performance."
What are the potential implications of using masked transformers in other machine learning applications
他の機械学習アプリケーションへのマスクトランスフォーマーの利用は多岐に渡ります。例えば自然言語処理(NLP)ではBERT(Bidirectional Encoder Representations from Transformers)モデルが広く使用されており、その中でもマスクトレーニングは単語予測タスクなどで活躍しています。画像生成分野以外でも音声処理や時系列データ解析など幅広い領域で応用可能です。これらの領域では局所的・部分的情報から全体像を推定する必要がある場面があり、そうした場面でマスキング技術は有益です。
How can the concept of masked training be applied to different types of neural networks beyond transformers