Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

Resume:

1.- Alan Turing's ideas on using machines to mimic the human mind, including the Turing test, universality of machines, memory requirements, and machine learning.

2.- Brains as decision-making devices that receive complex data and utilize it to make good decisions, with a focus on vision.

3.- Supervised learning approach of defining a task, collecting human decisions, and training machines to generate the same responses.

4.- ImageNet challenge of assigning images to 1000 categories. Performance has improved from 50% to 90% accuracy over 10 years.

5.- Question of whether high ImageNet performance implies brain-like visual decision making in neural networks.

6.- Testing generalization ability as a key to intelligence when input data or task changes.

7.- Transfer learning: Reusing features from pretrained ImageNet models as fixed representations for other vision tasks.

8.- Successes of transfer learning in saliency prediction, pose estimation, behavior tracking, showing useful generalization beyond ImageNet.

9.- Limitations: Adversarial examples, sensitivity to domains/backgrounds, texture bias show ImageNet features alone don't imply brain-like vision.

10.- Controlled experiments showing CNNs rely more on texture while humans rely more on shape for object recognition.

11.- Out-of-domain testing on noise perturbations reveals non-human-like sensitivity of standard CNNs compared to shape-based models.

12.- Removing texture bias through data augmentation makes CNNs more robust to noise like humans.

13.- Empirical findings that better out-of-domain accuracy on some datasets correlates with more human-like decisions.

14.- Counterfactual testing of smallest input changes that alter model decisions is even stronger test of human-like vision.

15.- Generative modeling enables this on MNIST, revealing human-interpretable perturbations at class boundaries.

16.- Controversial stimuli experiments systematically comparing model disagreements enables quantifying alignment with human decisions.

17.- Generative model (ABS) shows best alignment with human interpretation of ambiguous digits.

18.- Scaling up generative models to natural images requires handling combinatorial complexity of objects and scenes.

19.- Compositional generative scene model learns to sequentially render background and objects from noisy unsupervised segmentation.

20.- Learned latent representation captures meaningful perceptual properties, enables plausible recombination and intervention on scenes.

21.- Exploring invariance manifolds in neural networks to study what information is preserved or discarded.

22.- Invertible neural networks allow synthesizing "metameric" images with same output but different nuisance (non-class-specific) information.

23.- For standard CNNs, humans perceive metamers as identical to nuisance image, not class image, exposing misaligned invariances.

24.- Modified training to encourage nuisance space to be invariant to class improves human consistency of CNN invariances.

25.- Actively shaping invariances in neural networks is important direction to make their decisions more human-like.

26.- Overall perspective on using data and inductive biases to constrain learnable decision rules towards intended human-like solutions.

27.- Object-centric generative models and compositionality across scales as key ingredients for generating training data.

28.- Implicit argument that more human-like decision making, not just benchmark performance, should be goal of computer vision.

29.- Importance of out-of-domain and counterfactual testing to assess and improve human-consistency of vision models.

30.- Central role of generative models in future work to build more robust, generalizable and human-aligned computer vision systems.

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