For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/08/how-transformers-are-changing-the-direction-of-deep-learning-architectures-a-presentation-from-synopsys/
Tom Michiels, System Architect for DesignWare ARC Processors at Synopsys, presents the “How Transformers are Changing the Direction of Deep Learning Architectures” tutorial at the May 2022 Embedded Vision Summit.
The neural network architectures used in embedded real-time applications are evolving quickly. Transformers are a leading deep learning approach for natural language processing and other time-dependent, series data applications. Now, transformer-based deep learning network architectures are also being applied to vision applications with state-of-the-art results compared to CNN-based solutions.
In this presentation, Michiels introduces transformers and contrast them with the CNNs commonly used for vision tasks today. He examines the key features of transformer model architectures and shows performance comparisons between transformers and CNNs. He concludes the presentation with insights on why Synopsys thinks transformers are an important approach for future visual perception tasks.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
http://arxiv.org/abs/1609.08144
を読んでみたので、簡単にまとめました。間違い等は是非ご指摘ください。
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/08/how-transformers-are-changing-the-direction-of-deep-learning-architectures-a-presentation-from-synopsys/
Tom Michiels, System Architect for DesignWare ARC Processors at Synopsys, presents the “How Transformers are Changing the Direction of Deep Learning Architectures” tutorial at the May 2022 Embedded Vision Summit.
The neural network architectures used in embedded real-time applications are evolving quickly. Transformers are a leading deep learning approach for natural language processing and other time-dependent, series data applications. Now, transformer-based deep learning network architectures are also being applied to vision applications with state-of-the-art results compared to CNN-based solutions.
In this presentation, Michiels introduces transformers and contrast them with the CNNs commonly used for vision tasks today. He examines the key features of transformer model architectures and shows performance comparisons between transformers and CNNs. He concludes the presentation with insights on why Synopsys thinks transformers are an important approach for future visual perception tasks.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
http://arxiv.org/abs/1609.08144
を読んでみたので、簡単にまとめました。間違い等は是非ご指摘ください。
10. 9
評価方法について
【気象の分野でよく使われる評価指標】
→ 霧は極度の不均衡データになっている
■スレットスコア(TS) = D / ( B + C + D )
このうち適当に予測しても当たってしまう
割合を考慮したものをETSと呼ぶ
●バイアススコア(BI) = ( C + D ) / ( B + D )
空振り・見逃しのバランスを見る
【ベンチマーク】
気象庁の視程ガイダンスの精度:成田空港はETS = 0.08 (±0.02)
ここで霧かどうかの閾値は 1600m となっている
→ 分析[1]の閾値も 1600m とする
※気象庁の手法はカルマンフィルター+頻度バイアス補正
通常公開されていないデータも使える
→ 基本、不利な戦いではある
予報
0(なし) 1(あり)
実況
0(なし) A C
1(あり) B D