The End Of Knowledge - Vault 2 - ICLR (2014-2023)

graph LR classDef adversarial fill:#f9d4d4, font-weight:bold, font-size:14px; classDef gans fill:#d4f9d4, font-weight:bold, font-size:14px; classDef applications fill:#d4d4f9, font-weight:bold, font-size:14px; classDef robustness fill:#f9f9d4, font-weight:bold, font-size:14px; classDef efficiency fill:#f9d4f9, font-weight:bold, font-size:14px; A[Ian Goodfellow
ICLR 2019] --> B[Adversarial ML: game theory,
competing costs, Nash equilibrium 1] A --> C[GANs: generate samples,
minimax generator-discriminator game 2] C --> D[GANs: rapid improvement,
high-quality complex images 3] C --> E[GANs: unsupervised image-to-image
translation without paired examples 4] C --> F[GANs: temporally coherent
video generation, dance transfer 5] C --> G[GANs: realistic image generation
without photorealistic skills 6] C --> H[GANs: physical applications,
3D dental crowns, fashion 7] C --> I[GANs: scaling, style transfer,
reduced supervision 8] A --> J[Secure ML: robustness to
adversarial examples, violated IID 9] J --> K[Adversarial training: attacker-model
equilibrium, key current defense 10] A --> L[Model-based optimization: challenging
due to adversarial examples 11] L --> M[Model-based optimization: designed
genes/proteins, medical potential 12] A --> N[RL: adversarial self-play,
historically and in AlphaGo 13] N --> O[RL: non-symmetric games,
producing useful side-effects 14] N --> P[RL adversarial examples:
perturbing Atari frames 15] N --> Q[RL: GAN-like reward learning,
specifying complex tasks 16] A --> R[Extreme reliability: exceeding
99% accuracy, worst-case focus 17] R --> S[Worst-case curves may enable
provable robustness guarantees 18] R --> T[Adversarial robustness verification
applied to air traffic control 19] A --> U[Label efficiency crucial,
labeling expensive or risky 20] U --> V[Semi-supervised GANs leverage
unlabeled data, improve efficiency 21] class A,B adversarial; class C,D,E,F,G,H,I gans; class J,K,R,S,T robustness; class L,M,N,O,P,Q applications; class U,V efficiency;

Resume:

1.-Adversarial machine learning uses game theory instead of just optimization. Players have competing costs and seek a Nash equilibrium.

2.-Generative Adversarial Networks (GANs) generate new samples from a data distribution via a minimax game between generator and discriminator.

3.-GANs have rapidly improved since 2014 to generate high-quality, high-resolution images from complex datasets like ImageNet.

4.-Unsupervised image-to-image translation with GANs, like turning day scenes to night, is possible without paired examples.

5.-Video generation GANs can make temporally coherent videos, e.g. transferring dance moves between people.

6.-GANs enable creativity in realistic image generation without requiring photorealistic artistic skills, via labeled semantic maps.

7.-GANs are starting to have physical world applications like 3D printed dental crowns. Fashion design is a future possibility.

8.-Recent GAN advances include scaling up models, incorporating style transfer, and reducing supervision to ~10% labeled data.

9.-For secure ML, models must be robust when the IID assumption is violated by an attacker's adversarial examples.

10.-Adversarial training, finding an equilibrium between attacker and model, is a key current defense against adversarial examples.

11.-Model-based optimization, e.g. maximizing a learned surrogate model's prediction, is challenging due to adversarial examples but has potential.

12.-Model-based optimization has designed genes/proteins that bind well in simulation. Real-world medical applications may follow.

13.-Reinforcement learning has always involved adversarial self-play, e.g. Arthur Samuel's 1950s checkers agent.

14.-Self-play RL remains important in systems like AlphaGo, but non-symmetric games and producing useful side-effects are key recent focuses.

15.-Adversarial examples for RL exist, e.g. perturbing Atari frames to degrade agent performance a few timesteps later.

16.-Learning reward functions with GAN-like models can help specify complex RL tasks, e.g. a robot painting specific characters.

17.-Extreme reliability needs more than current ~99% ML accuracy. Adversarial ML's worst-case focus may help, like in robust distributed systems.

18.-Analyzing worst-case performance curves may enable provable robustness guarantees exceeding observed average performance.

19.-Verification tools originally for proving adversarial robustness are being applied to practical systems like air traffic control.

20.-Improving label efficiency is crucial since labeling is often expensive or risky, e.g. in healthcare.

21.-Semi-supervised GANs with a classifier discriminator can leverage unlabeled data to improve sample efficiency.

Knowledge Vault built byDavid Vivancos 2024