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Sub-Question Importance-aware Network Tuning
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Classroom Contents
Explaining Model Decisions and Fixing Them Through Human Feedback
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- 1 Intro
- 2 Interpretability in different stages of Al evolution
- 3 Approaches for visual explanations
- 4 Visualize any decision
- 5 Visualizing Image Captioning models
- 6 Visualizing Visual Question Answering models
- 7 Analyzing Failure modes
- 8 Grad-CAM for predicting patient outcomes
- 9 Extensions to Multi-modal Transformer based Architectures
- 10 Desirable properties of Visual Explanations
- 11 Equalizer
- 12 Biases in Vision and Language models
- 13 Human Importance-aware Network Tuning (HINT)
- 14 Contrastive Self-Supervised Learning (SSL)
- 15 Why SSL methods fail to generalize to arbitrary images?
- 16 Does improved SSL grounding transfer to downstream tasks?
- 17 CAST makes models resilient to background changes
- 18 VQA for visually impaired users
- 19 Sub-Question Importance-aware Network Tuning
- 20 Explaining Model Decisions and Fixing them via Human Feedback
- 21 Grad-CAM for multi-modal transformers
