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{
"metadata": {
"source": "Kaiming He Papers Analysis",
"date": "2026-01-26",
"papers_analyzed": 11,
"analysis_method": "Text extraction and pattern mining",
"latest_addition": {
"papers": ["Mean Flows", "ViTDet", "MoCo v2", "Deconstructing Denoising Diffusion Models", "Autoregressive Image Generation (MAR)"],
"extraction_date": "2026-01-26",
"new_knowledge_files": [
"theory-driven-papers-kaiming-he.md",
"design-simplification-papers-kaiming-he.md"
]
}
},
"knowledge_files": {
"structure.md": {
"status": "updated",
"last_update": "2026-01-26",
"contains": "Basic structure patterns from 19 Kaiming He papers"
},
"writing-techniques.md": {
"status": "needs_update",
"last_update": "2026-01-26",
"contains": "Basic writing techniques from 19 Kaiming He papers"
},
"rethinking-papers-kaiming-he.md": {
"status": "complete",
"focus": "Rethinking papers, challenging conventional wisdom",
"source_paper": "Autoregressive Image Generation without Vector Quantization (NeurIPS 2024 Spotlight)"
},
"theory-driven-papers-kaiming-he.md": {
"status": "new",
"focus": "Theory-driven papers, first principles, MeanFlow Identity",
"source_paper": "Mean Flows for One-step Generative Modeling (2025)"
},
"design-simplification-papers-kaiming-he.md": {
"status": "new",
"focus": "Design simplification, minimal adaptations, 'Surprisingly' findings",
"source_paper": "Exploring Plain Vision Transformer Backbones for Object Detection (ViTDet, ECCV 2022)"
}
},
"patterns_extracted": {
"introduction_frameworks": {
"principle_introduction": {
"source": "MeanFlows",
"pattern": "Background → Problem → Critique (Despite...) → Core Concept → Theory → Advantage → Results",
"keywords": ["principled", "intrinsic", "well-defined", "naturally", "first principles"]
},
"challenge_assumptions": {
"source": "ViTDet",
"pattern": "Traditional → New Challenge → Common Solution → Our Direction → Philosophy → Surprisingly → Implications",
"keywords": ["minimal adaptations", "sufficient", "decouple", "independence", "surprisingly"]
},
"rethinking_conventional_wisdom": {
"source": "MAR",
"pattern": "Conventional wisdom → Question → Analysis → Alternative → Results → Vision",
"keywords": ["Conventional wisdom holds that", "Is it necessary", "not a necessity"]
}
},
"surprisingly_findings": {
"level_1": {
"pattern": "Surprisingly, we observe: (i)... and (ii)...",
"usage": "First-level surprise - basic findings",
"example": "ViTDet Abstract"
},
"level_2": {
"pattern": "More surprisingly, under some circumstances...",
"usage": "Second-level surprise - competitive results",
"example": "ViTDet Introduction"
},
"level_3": {
"pattern": "With [condition], outperforms... gains more prominent for...",
"usage": "Third-level surprise - superiority under conditions",
"example": "ViTDet Introduction"
},
"variants": {
"interestingly": "Observation + literature support + explanation",
"notably": "Important detail or counter-intuitive result",
"it_is_worth_noting": "Technical caveat or clarification"
}
},
"ablation_techniques": {
"incremental_tables": {
"pattern": "Baseline → (a) → (b) → (c) with Δ标注",
"source": "ViTDet Table 1"
},
"destructive_comparison": {
"pattern": "Intentionally wrong values to prove necessity",
"source": "MeanFlows Table 1b"
},
"narrative_structure": {
"observation_then_explain": "Observe pattern → Provide explanation (literature/hypothesis/theory)"
}
},
"theoretical_derivation": {
"naming_identity": {
"pattern": "Define → Derive → Name ('X Identity')",
"source": "MeanFlows MeanFlow Identity"
},
"step_by_step": {
"pattern": "Motivation → Derivation with 'Now we...' → Justification with 'where...'",
"source": "MeanFlows Section 2"
}
},
"comparison_techniques": {
"principled_vs_heuristic": {
"pattern": "At the core...does not depend on...In contrast, typically rely on...",
"source": "MeanFlows"
},
"fair_comparison_declaration": {
"pattern": "Admit complexity → Claim effort → Demonstrate fairness",
"source": "ViTDet"
},
"multi_factor_analysis": {
"pattern": "Factors identified → Trend behavior → Wall-clock time",
"source": "ViTDet Results"
}
},
"keyword_strategies": {
"theory_paper": ["principled", "intrinsic", "well-defined", "naturally", "self-contained", "solely originated from"],
"design_paper": ["minimal", "sufficient", "decouple", "independence", "surprisingly", "abandons"],
"rethinking_paper": ["Conventional wisdom holds that", "not a necessity", "orthogonal to", "uncharted realm"]
}
},
"papers_analyzed_list": [
"Non-local Neural Networks",
"SlowFast Networks",
"Rethinking ImageNet Pre-training",
"Faster R-CNN",
"Delving Deep into Rectifiers (PReLU)",
"Spatial Pyramid Pooling (SPP-net)",
"Deconstructing Denoising Diffusion Models",
"Autoregressive Image Generation without Vector Quantization (MAR)",
"Mean Flows for One-step Generative Modeling",
"Exploring Plain Vision Transformer Backbones for Object Detection (ViTDet)",
"MoCo v2: Improved Baselines with Momentum Contrastive Learning"
],
"integration_summary": {
"total_papers": 11,
"knowledge_files": 5,
"patterns_extracted": 25,
"paper_types_identified": [
"Theory-driven (MeanFlows)",
"Design simplification (ViTDet)",
"Rethinking (MAR)",
"Deconstruction (DDM)",
"Milestone (PReLU)",
"Multi-task (SPP-net)",
"Technical note (MoCo v2)"
]
}
}
|
