The End Of Knowledge - Vault 6/48 - CVPR - 2019 - Are All Features Created Equal?

graph LR classDef main fill:#f9d4f9, font-weight:bold, font-size:14px classDef deep_learning fill:#f9d4d4, font-weight:bold, font-size:14px classDef adversarial fill:#d4f9d4, font-weight:bold, font-size:14px classDef robust_models fill:#d4d4f9, font-weight:bold, font-size:14px classDef applications fill:#f9f9d4, font-weight:bold, font-size:14px classDef future fill:#d4f9f9, font-weight:bold, font-size:14px Main[Are All Features
Created Equal?] --> A[Deep Learning
Fundamentals] Main --> B[Adversarial Examples
and Features] Main --> C[Robust Models
and Training] Main --> D[Applications of
Robust Models] Main --> E[Future Directions
and Challenges] A --> A1[Deep learning outperforms humans
on ImageNet 1] A --> A2[Deep learning creates meaningful
data representations 2] A --> A3[Challenges: adversarial examples, generative
instability 3] A --> A4[Models use both feature
types 8] A --> A5[Data contains robust and
non-robust features 7] A --> A6[Non-robust features are actually
data-predictive 23] B --> B1[Imperceptible perturbations fool classifiers 4] B --> B2[Mislabeled adversarial data still
performs well 5] B --> B3[Adversarial perturbations may be
meaningful features 6] B --> B4[Robust adversarial examples more
human-interpretable 12] B --> B5[Visualizing neurons rationalizes model
mistakes 18] B --> B6[Non-robust features nature poorly
understood 30] C --> C1[Robust ML avoids non-robust
features 9] C --> C2[Robust models align with
human perception 10] C --> C3[Robust saliency maps match
human expectations 11] C --> C4[Robust models capture semantic
similarity better 13] C --> C5[Robust neurons correspond to
interpretable features 16] C --> C6[Robust training improves model
interpretability 24] D --> D1[Simple optimizations visualize robust
representations 14] D --> D2[Robust models enable semantic
image interpolations 15] D --> D3[Robust models allow specific
attribute additions 17] D --> D4[Robust classifier performs various
vision tasks 19] D --> D5[Optimization generates images from
random noise 20] D --> D6[Robust classifiers perform super-resolution,
in-painting 21] E --> E1[Interactive class manipulation using
robust classifiers 22] E --> E2[Understanding decisions requires human-sensible
features 25] E --> E3[Robust representations enable simple
vision solutions 26] E --> E4[Robustness useful beyond security,
reliability 27] E --> E5[Refine robustness definition for
human alignment 28] E --> E6[Determining right perturbations remains
challenging 29] class Main main class A,A1,A2,A3,A4,A5,A6 deep_learning class B,B1,B2,B3,B4,B5,B6 adversarial class C,C1,C2,C3,C4,C5,C6 robust_models class D,D1,D2,D3,D4,D5,D6 applications class E,E1,E2,E3,E4,E5,E6 future

Resume:

1.- Deep learning has made significant progress on key problems like ImageNet classification, outperforming humans.

2.- Deep learning is believed to create meaningful data representations and generate semantically meaningful embeddings.

3.- Despite successes, there are challenges like brittleness to adversarial examples and instability in generative models.

4.- Adversarial examples can fool classifiers with imperceptible perturbations, raising questions about model robustness.

5.- An experiment showed classifiers trained on adversarially perturbed, mislabeled data still performed well on original test sets.

6.- This suggests adversarial perturbations may correspond to meaningful features, not just aberrations.

7.- Data contains robust features (used by humans) and non-robust features (brittle but useful for generalization).

8.- Models use both robust and non-robust features to maximize accuracy, making them vulnerable to adversarial attacks.

9.- Robust ML aims to force models to avoid leveraging non-robust features, changing the prior on feature use.

10.- Robust models may have lower accuracy and need more training data, but offer benefits in alignment with human perception.

11.- Robust models produce saliency maps that better align with human expectations of important image regions.

12.- Visualizations of adversarial examples for robust models show more human-interpretable feature changes.

13.- Robust models have better feature representations, capturing semantic similarity more consistently than standard models.

14.- Robust representations enable simple feature manipulations and visualizations using basic optimization techniques.

15.- Semantic interpolations between images can be easily created using robust model representations.

16.- Individual neurons in robust models often correspond to human-interpretable features.

17.- Feature manipulation in robust models allows adding specific attributes to images, like stripes.

18.- Robust models' mistakes can be rationalized by visualizing the neurons responsible for incorrect classifications.

19.- A single robust classifier can perform various computer vision tasks previously requiring complex generative models.

20.- Simple optimization techniques with robust classifiers can generate realistic images from random noise.

21.- Robust classifiers can perform tasks like super-resolution and in-painting with simple optimization.

22.- Interactive image class manipulation is possible using robust classifiers and optimization.

23.- Adversarial examples reveal models' dependence on non-robust features, which are actually predictive of data.

24.- Robust training imposes a prior that aligns more closely with human vision and improves model interpretability.

25.- Understanding model decisions requires forcing them to use features that make sense to humans.

26.- Robust representations enable simple solutions to various computer vision tasks using basic optimization techniques.

27.- Robustness should be considered a tool for any machine learning task, not just for security or reliability.

28.- The definition of robustness may need refinement to better align with human perception and exclude undesired features.

29.- Determining the right set of perturbations for robustness is an ongoing challenge in the field.

30.- Non-robust features are real patterns in data, but their nature and appearance remain poorly understood.

Knowledge Vault built byDavid Vivancos 2024