本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
ICML2018読み会: Overview of NLP / Adversarial AttacksMotoki Sato
ICML 2018読み会の資料.
Overview of NLP/ Adversarial Attacks
- Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples
- Synthesizing Robust Adversarial Examples
- Black-box Adversarial Attacks with Limited Queries and Information
論文紹介:Dueling network architectures for deep reinforcement learningKazuki Adachi
Wang, Ziyu, et al. "Dueling network architectures for deep reinforcement learning." Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1995-2003, 2016.
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
2. ドメイン転移と不変表現に関するサーベイ
• On Learning Invariant Representations on Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Domain Agnostic Learning with Disentangled Representations, ICML2019
• Transferabiliity vs. Discriminability: Batch Spectral Penaralization for Adversarial Domain Adaptation, ICML2019
• Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers, ICML2019
• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment, ICML2019
• Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization, ECML2019
• Bridging Theory and Algorithm for Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Learning to Generalize: Meta-Learning for Domain Generalization, AAAI2018
• MetaReg: Towards Domain Generalization using Meta-Regularization, NIPS2018
• Feature-Critic Networks for Heterogeneous Domain Generalization, ICML2019
• Unsupervised Adversarial Induction, NIPS2018
• Universal Domain Adaptation, CVPR2019
• Importance Weighted Adversarial Nets for Partial Domain Adaptation, CVPR2018
2
12. 以降の内容
• On Learning Invariant Representations on Domain Adaptation, ICML2019
• Domain Agnostic Learning with Disentangled Representations, ICML2019
• Transferabiliity vs. Discriminability: Batch Spectral Penaralization for Adversarial Domain Adaptation, ICML2019
• Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers, ICML2019
• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment, ICML2019
• Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization, ECML2019
• Bridging Theory and Algorithm for Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Learning to Generalize: Meta-Learning for Domain Generalization, AAAI2018
• MetaReg: Towards Domain Generalization using Meta-Regularization, NIPS2018
• Feature-Critic Networks for Heterogeneous Domain Generalization, ICML2019
• Unsupervised Adversarial Induction, NIPS2018
• Universal Domain Adaptation, CVPR2019
• Importance Weighted Adversarial Nets for Partial Domain Adaptation, CVPR2018
12
13. 以降の内容
• On Learning Invariant Representations on Domain Adaptation, ICML2019
• Domain Agnostic Learning with Disentangled Representations, ICML2019
• Transferabiliity vs. Discriminability: Batch Spectral Penaralization for Adversarial Domain Adaptation, ICML2019
• Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers, ICML2019
• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment, ICML2019
• Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization, ECML2019
• Bridging Theory and Algorithm for Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Learning to Generalize: Meta-Learning for Domain Generalization, AAAI2018
• MetaReg: Towards Domain Generalization using Meta-Regularization, NIPS2018
• Feature-Critic Networks for Heterogeneous Domain Generalization, ICML2019
• Unsupervised Adversarial Induction, NIPS2018
• Universal Domain Adaptation, CVPR2019
• Importance Weighted Adversarial Nets for Partial Domain Adaptation, CVPR2018
13
共通の問い:不変性を高めることは本当に良いことなのか?
19. On Learning Invariant Representations for Domain Adaptation, ICML2019
19
Han Zhao et al.
• Notationが違うが、Ben-Davidとの差は第3項
• ソースとターゲットに共通のラベリング関数を仮定しない
• Joint Errorは、ある特徴空間上での真のラベリング関数のミスマッチ
• ※ちなみにこの論文では対処法については議論してない
20. On Learning Invariant Representations for Domain Adaptation, ICML2019
20
Han Zhao et al.
Over-training hurt generalization!
理由:ラベル分布が異なる場合に学習しすぎると
真のラベリング関数がソースとターゲットでずれ
る(RTが途中から劣化!)
22. Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers, ICML2019
22
Hong Liu et al.
手法の概念図
• 特徴空間上を動かす代わりに、ドメイン識
別器を使って新たに事例を作る
• 特徴空間は変化しないので劣化しない
• 事例は、(1) ドメイン識別器を騙す、(2) Yの
分類平面も騙すような事例
(決定境界の近くに移す)
アルゴリズム
31. Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
31
Kaicho You et al.
IWCVの問題:アンバイアスだが分散が大きい
Renyi Divergence
提案法:Deep Embedded Validation
(1) 特徴空間上で密度比を計測する (ドメイン識別器を使う)
(2) Control Variatesを使う(平均をベースラインに使う)
32. Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
32
Kaicho You et al.
(1) 手法問わず使える (2) ターゲットとほぼ同等
(3) Control Variateは平均すると良い
35. メタ正則化によるドメイン転移
• On Learning Invariant Representations on Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Domain Agnostic Learning with Disentangled Representations, ICML2019
• Transferabiliity vs. Discriminability: Batch Spectral Penaralization for Adversarial Domain Adaptation, ICML2019
• Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers, ICML2019
• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment, ICML2019
• Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization, ECML2019
• Bridging Theory and Algorithm for Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Learning to Generalize: Meta-Learning for Domain Generalization, AAAI2018
• MetaReg: Towards Domain Generalization using Meta-Regularization, NIPS2018
• Feature-Critic Networks for Heterogeneous Domain Generalization, ICML2019
• Unsupervised Adversarial Induction, NIPS2018
• Universal Domain Adaptation, CVPR2019
• Importance Weighted Adversarial Nets for Partial Domain Adaptation, CVPR2018
35
共通の問い: 不変性という基準を設計する必要あるのか?
40. より複雑な問題設定への応用
• On Learning Invariant Representations on Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Domain Agnostic Learning with Disentangled Representations, ICML2019
• Transferabiliity vs. Discriminability: Batch Spectral Penaralization for Adversarial Domain Adaptation, ICML2019
• Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers, ICML2019
• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment, ICML2019
• Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization, ECML2019
• Bridging Theory and Algorithm for Domain Adaptation, ICML2019
• Toward Accurate Model Selection in Deep Unsupervised Domain Adaptation, ICML2019
• Learning to Generalize: Meta-Learning for Domain Generalization, AAAI2018
• MetaReg: Towards Domain Generalization using Meta-Regularization, NIPS2018
• Feature-Critic Networks for Heterogeneous Domain Generalization, ICML2019
• Universal Domain Adaptation, CVPR2019
• Importance Weighted Adversarial Nets for Partial Domain Adaptation, CVPR2018
• Unsupervised Adversarial Induction, NIPS2018
40
50. Related Works: Feature Adaptation
Mathematical Foundation
[Ganin, 2016] “Domain-Adversarial Training of Neural Networks”
Visualization
[Ben-David, 2010] “A theory of learning from different domains”
ドメイン間の距離ソース損失
理想的なhを使うと
きの損失の差
50