refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-pooling
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title: Pooling
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Max Pooling, Average Pooling, Global Pooling]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [deep-learning, cnn, pooling, downsampling]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch
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---
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# Pooling
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## 매 한 줄
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> **"매 spatial/sequence dimension downsample — invariance + receptive field 확대."**. CNN 시대의 staple (max/avg pool), 매 modern Transformer는 거의 안 씀 (strided conv 또는 attention pooling). Global pool은 여전히 classification head 표준.
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## 매 핵심
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### 매 종류
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- **Max Pooling**: window 내 max — translation invariance, edge-preserve.
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- **Average Pooling**: window 평균 — smooth, all-pixel contribute.
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- **Global Average Pooling (GAP)**: 매 entire feature map → 단일 값. ResNet/EfficientNet head.
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- **Adaptive Pooling**: output size fix → input size 무관 (PyTorch `AdaptiveAvgPool2d`).
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- **Attention Pooling**: weighted sum, learned weights — ViT [CLS] 또는 perceiver.
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- **L_p Pooling, Stochastic Pooling, Mixed Pooling**: less common, occasionally robust.
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### 매 왜 사용
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- **Downsampling**: spatial size 줄여 compute / params 감소.
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- **Invariance**: small translation에 robust.
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- **Receptive field 확대**: deeper layer가 wider context 봄.
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- **Overfitting 방지**: parameter-free regularization 효과.
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### 매 modern shift
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- 2020+ Transformer 시대 — 매 pool 자리에 strided conv (stage transition) 또는 patch merging (Swin) 또는 attention pooling.
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- ConvNeXt도 strided conv 사용.
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- GAP은 classification head에서 여전히 universal.
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## 💻 패턴
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### Max / Avg pool 기본
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```python
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import torch.nn as nn
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maxp = nn.MaxPool2d(kernel_size=2, stride=2) # H,W /2
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avgp = nn.AvgPool2d(kernel_size=2, stride=2)
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```
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### Global Average Pooling (classification head)
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```python
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import torch.nn as nn
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class Head(nn.Module):
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def __init__(self, c, n_cls):
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super().__init__()
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self.gap = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Linear(c, n_cls)
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def forward(self, x): # x: (B, C, H, W)
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x = self.gap(x).flatten(1) # (B, C)
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return self.fc(x)
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```
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### Adaptive pool (variable input size)
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```python
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import torch, torch.nn as nn
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pool = nn.AdaptiveAvgPool2d((7, 7)) # 항상 7x7 output
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x = torch.randn(2, 64, 33, 41) # 임의 spatial
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y = pool(x) # (2, 64, 7, 7)
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```
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### Attention Pooling (ViT [CLS])
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```python
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import torch, torch.nn as nn
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class AttnPool(nn.Module):
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def __init__(self, d, heads=8):
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super().__init__()
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self.q = nn.Parameter(torch.randn(1, 1, d))
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self.attn = nn.MultiheadAttention(d, heads, batch_first=True)
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def forward(self, x): # x: (B, N, D)
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B = x.size(0)
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q = self.q.expand(B, -1, -1)
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out, _ = self.attn(q, x, x)
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return out.squeeze(1) # (B, D)
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```
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### Patch Merging (Swin Transformer)
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```python
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import torch, torch.nn as nn
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class PatchMerging(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.norm = nn.LayerNorm(4*dim)
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self.reduction = nn.Linear(4*dim, 2*dim, bias=False)
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def forward(self, x): # x: (B, H, W, C)
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x0 = x[:, 0::2, 0::2, :]; x1 = x[:, 1::2, 0::2, :]
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x2 = x[:, 0::2, 1::2, :]; x3 = x[:, 1::2, 1::2, :]
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x = torch.cat([x0,x1,x2,x3], -1)
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return self.reduction(self.norm(x))
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```
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### 1D pool (sequence / audio)
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```python
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import torch.nn as nn
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pool1d = nn.MaxPool1d(kernel_size=4, stride=4) # (B, C, T) -> (B, C, T/4)
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gap1d = nn.AdaptiveAvgPool1d(1)
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```
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### Set/Graph pooling (mean/max/sum)
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```python
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import torch
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def set_mean(x, mask): # x:(B,N,D), mask:(B,N)
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m = mask.unsqueeze(-1).float()
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return (x*m).sum(1) / m.sum(1).clamp(min=1)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Classification final feature | Global Avg Pooling |
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| Variable input image | AdaptiveAvgPool2d |
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| Edge-preserve detection | Max Pool 또는 strided conv |
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| Transformer stage transition | Patch merging / strided conv |
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| Set/sequence aggregation | Attention pool |
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| Audio waveform | 1D max/avg pool 또는 strided conv |
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**기본값**: feature map → GAP, downsample → strided conv (modern).
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## 🔗 Graph
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- 부모: [[Deep Learning]]
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- 변형: [[Max_Pooling]] · [[Average_Pooling]]
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- 응용: [[Image-Classification-Mastery]] · [[ResNet]] · [[ViT]]
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## 🤖 LLM 활용
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**언제**: CNN backbone에서 spatial reduce, classification head GAP, set/graph aggregation.
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**언제 X**: dense prediction (segmentation, detection)에서 매 정보 손실 — skip connection 결합 또는 dilated conv 고려.
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## ❌ 안티패턴
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- **Pool then upsample for segmentation without skip**: 매 detail 손실. U-Net skip 사용.
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- **MaxPool everywhere in modern arch**: 매 strided conv가 매 학습 가능 — 거의 dominant.
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- **Flatten without GAP**: classification head fully-connected로 들어가면 매 huge params + overfit.
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- **Pool over tokens with [CLS] available**: attention pool 또는 [CLS] readout 매 better.
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## 🧪 검증 / 중복
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- Verified (PyTorch docs nn.MaxPool2d, AdaptiveAvgPool, Swin Transformer paper, ConvNeXt paper).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — pooling types + modern shift to strided conv / attention pool |
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