Initial commit — generate-hypothesis skill

Standalone Claude Code skill repo extracted from CVGL vault
(.claude/skills/generate-hypothesis/). Generates testable
If/Then/Because hypotheses for the NADEZHDA / SOFIA research
project from the literature library.

Contents:
- SKILL.md             — behaviour spec (6-phase pipeline, output contract)
- README.md            — human-facing entry: when to use, install, examples
- templates/hypothesis_full.md     — full template (8 required sections)
- templates/hypothesis_compact.md  — short draft template
- reference/method_taxonomy.md     — methods to NADEZHDA components mapping

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

reference/method_taxonomy.md

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+# Таксономия методов по компонентам NADEZHDA
+
+Справочник для скилла `/generate-hypothesis`: какие методы из литературы применимы к каким компонентам системы.
+
+## Teacher backbone (DINOv3-L, ~300M frozen, patch 16×16)
+
+| Метод | Источник | Применимость |
+|:------|:---------|:-------------|
+| **DINOv3-L** (SAT-493M pretraining, Register Tokens, Gram Anchoring) | F4_BB DINOv3 | **Текущий выбор** — превосходит DINOv2: +6.4 mIoU dense, satellite-native |
+| DINOv2-L self-supervised | Устаревший | Заменён на DINOv3-L |
+| GeoRSCLIP (RS-specific VL) | F2_BB | Альтернатива: domain-specific VL pretraining |
+| CrossEarth (geospatial FM) | F3_BB | Альтернатива: explicit RS domain |
+| Perception Encoder | B13 | Best features at intermediate layers |
+
+**Технические особенности DINOv3 vs DINOv2:**
+- Patch: 16×16 (не 14×14) → 256/16 = 16×16 = 256 токенов (точно)
+- Register Tokens: 4 шт. → отфильтровать перед FPN
+- Outlier dimensions: ОБЯЗАТЕЛЬНО LayerNorm перед loss-функциями
+- Frozen > fine-tuned (by design) → не нужен LoRA
+
+## Student backbone (FastViT-T12, ~8.5M, weight-shared)
+
+| Метод | Источник | Применимость |
+|:------|:---------|:-------------|
+| FastViT-T12 | HYP_Student | Текущий выбор, weight-shared drone/sat |
+| MambaVision-T | B12 | Гибрид Mamba+ViT, 4.4 GFLOPs, TensorRT |
+| Efficient VMamba | B6 | Atrous selective scan, <50 GFLOPs |
+| GroupMamba | B11 | Group-based SSM, ещё легче |
+| LEGNet | B16 | 5-8M params, <5 GFLOPs — target-size |
+| Channel Group Pooling (CGP) | P1 VimGeo | Замена FC, экономия ~40% params |
+| RepViT-M1.1 | Benchmarking FM | 8.2M, 1.7 GFLOPs |
+
+## Fusion module (Multi-FiLM-Fusion)
+
+| Метод | Источник | Применимость |
+|:------|:---------|:-------------|
+| Multi-FiLM (γ·F + β) | F14 WeatherPrompt, SSF TPAMI | Текущий выбор, <0.7% overhead |
+| Bottleneck Cross-Attention | GLEAM M3 | Sparse tokens (8/modality) |
+| Coupled Mamba | NeurIPS 2024 | Inter-modal SSM state transition, 49% faster |
+| Cross-Selective SSM Scan | Sigma WACV 2025 | Mamba-native fusion |
+| Mutable Token + Dropout | P50 MMGeo | Graceful degradation при отсутствии модальности |
+| MoE (Flex-MoE) | NeurIPS Spotlight | Arbitrary modality combinations |
+| Gated Multimodal Unit | Pipeline v1 | Sigmoid gates + weighted sum |
+
+## Loss functions (7 losses + GradNorm)
+
+| Loss | Формула | Источник | Роль |
+|:-----|:--------|:---------|:-----|
+| L_task | Symmetric InfoNCE (τ learnable, ls=0.1) | P10 Sample4Geo | Основная retrieval |
+| L_LUPI | MSE(z_S, sg(z_T_fused)) | R9 Vapnik, R6 MobileGeo | Privileged KD |
+| L_feat | MSE(proj(F_S), sg(F_T)) через Conv1×1 | R1, R14 | Intermediate distill |
+| L_RKD | |d_S(i,j) - d_T(i,j)|² | R1 | Relational KD |
+| L_seg | hard CE + soft KL | Pipeline v2 | Segmentation distill |
+| L_CVD_MI | HSIC penalty | disentangle.py | Content-viewpoint separation |
+| L_CVD_Recon | Cross-view MSE | disentangle.py | Reconstruction from content only |
+| GradNorm | Adaptive λ balancing | Pipeline v2 | Gradient dominance prevention |
+| PALW | Progressive attenuate→enhance | P47 DPHR | Альтернатива GradNorm |
+| DWBL | Dynamic weighted batch-tuple | P1 VimGeo | Hard negative emphasis |
+
+## Distillation strategies
+
+| Стратегия | Источник | Compression | R@1 |
+|:----------|:---------|:-----------|:----|
+| MobileGeo hierarchical KD | R6 | 60× | 97.15% (Univ-1652) |
+| QDFL frozen + adapters | P62 | <5M adapter | 95.00% |
+| LUPI + multi-modal teacher | R9, R3 | Theoretical | — |
+| Teacher-Assistant chain | R15 MST-Distill | При capacity gap | — |
+| Self-distillation | R4 GeoDistill | Geometry-guided | — |
+
+## Edge deployment (Jetson Orin NX)
+
+| Техника | Источник | Эффект |
+|:--------|:---------|:-------|
+| INT8 TensorRT | R5, R12 | 2-3× speedup, 1-2% R@1 drop |
+| k-scaled PTQ для Mamba | R5 | FP16 state transitions + INT8 weights |
+| All-adder networks | R12 Q-A²NN | mul→add (экспериментально) |
+| Structured pruning | R16 survey | 5% incremental |
+| ONNX export | B12 MambaVision | Native TensorRT support |
+
+## Data augmentation
+
+| Техника | Источник | Эффект |
+|:--------|:---------|:-------|
+| CHSG layout simulation | P11 GeoDTR+ | +16-22% cross-domain R@1 |
+| Orientation randomization | P16 ConGeo | Предотвращает 16% collapse |
+| Weather simulation (10 conditions) | M1 WeatherPrompt | All-weather robustness |
+| GPS→DSS hard negatives | P10 Sample4Geo | k=64, K=128, refresh every 4 epochs |
+| Modality dropout p=0.3 | P50 MMGeo | LUPI compatibility |
+
+## Датасеты
+
+| Датасет | Роль в проекте | Размер | Ключевая метрика |
+|:--------|:---------------|:-------|:-----------------|
+| University-1652 | Основной бенчмарк | 142K imgs | D→S R@1 (SOTA: 97.50%) |
+| GeoText-1652 | Text modality training | 316K+ texts | Text→Image R@1 |
+| GTA-UAV/Game4Loc | Pretraining | 33.7K+14.6K | Zero-shot transfer |
+| UAV-VisLoc | Real-world validation | 6,742 drone | Continuous map matching |
+| World-UAV | Multi-country test | 927K imgs | Rotation/height robustness |
+| CVUSA | Cross-view benchmark | 35K pairs | R@1 (SOTA: 98.97%) |
+| CVACT | Cross-domain test | 128K pairs | R@1 (SSM лучше: 81.69%) |
+| VIGOR | Hardest benchmark | 90K+105K | Same-area R@1 |
+| SUES-200 | Multi-altitude | 24K real | AP across 4 heights |