2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/Pooling.md

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id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-pooling	Pooling	10_Wiki/Topics	verified	self	Max Pooling Average Pooling Global Pooling	none	A	0.9	applied	deep-learning cnn pooling downsampling		2026-05-10	pending	language framework python pytorch

Pooling

매 한 줄

"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.

매 핵심

매 종류

• Max Pooling: window 내 max — translation invariance, edge-preserve.
• Average Pooling: window 평균 — smooth, all-pixel contribute.
• Global Average Pooling (GAP): 매 entire feature map → 단일 값. ResNet/EfficientNet head.
• Adaptive Pooling: output size fix → input size 무관 (PyTorch AdaptiveAvgPool2d).
• Attention Pooling: weighted sum, learned weights — ViT [CLS] 또는 perceiver.
• L_p Pooling, Stochastic Pooling, Mixed Pooling: less common, occasionally robust.

매 왜 사용

• Downsampling: spatial size 줄여 compute / params 감소.
• Invariance: small translation에 robust.
• Receptive field 확대: deeper layer가 wider context 봄.
• Overfitting 방지: parameter-free regularization 효과.

매 modern shift

• 2020+ Transformer 시대 — 매 pool 자리에 strided conv (stage transition) 또는 patch merging (Swin) 또는 attention pooling.
• ConvNeXt도 strided conv 사용.
• GAP은 classification head에서 여전히 universal.

💻 패턴

Max / Avg pool 기본

import torch.nn as nn
maxp = nn.MaxPool2d(kernel_size=2, stride=2)   # H,W /2
avgp = nn.AvgPool2d(kernel_size=2, stride=2)

Global Average Pooling (classification head)

import torch.nn as nn
class Head(nn.Module):
    def __init__(self, c, n_cls):
        super().__init__()
        self.gap = nn.AdaptiveAvgPool2d(1)
        self.fc  = nn.Linear(c, n_cls)
    def forward(self, x):           # x: (B, C, H, W)
        x = self.gap(x).flatten(1)  # (B, C)
        return self.fc(x)

Adaptive pool (variable input size)

import torch, torch.nn as nn
pool = nn.AdaptiveAvgPool2d((7, 7))  # 항상 7x7 output
x = torch.randn(2, 64, 33, 41)       # 임의 spatial
y = pool(x)  # (2, 64, 7, 7)

Attention Pooling (ViT [CLS])

import torch, torch.nn as nn
class AttnPool(nn.Module):
    def __init__(self, d, heads=8):
        super().__init__()
        self.q = nn.Parameter(torch.randn(1, 1, d))
        self.attn = nn.MultiheadAttention(d, heads, batch_first=True)
    def forward(self, x):  # x: (B, N, D)
        B = x.size(0)
        q = self.q.expand(B, -1, -1)
        out, _ = self.attn(q, x, x)
        return out.squeeze(1)  # (B, D)

Patch Merging (Swin Transformer)

import torch, torch.nn as nn
class PatchMerging(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.norm = nn.LayerNorm(4*dim)
        self.reduction = nn.Linear(4*dim, 2*dim, bias=False)
    def forward(self, x):  # x: (B, H, W, C)
        x0 = x[:, 0::2, 0::2, :]; x1 = x[:, 1::2, 0::2, :]
        x2 = x[:, 0::2, 1::2, :]; x3 = x[:, 1::2, 1::2, :]
        x = torch.cat([x0,x1,x2,x3], -1)
        return self.reduction(self.norm(x))

1D pool (sequence / audio)

import torch.nn as nn
pool1d = nn.MaxPool1d(kernel_size=4, stride=4)  # (B, C, T) -> (B, C, T/4)
gap1d  = nn.AdaptiveAvgPool1d(1)

Set/Graph pooling (mean/max/sum)

import torch
def set_mean(x, mask):  # x:(B,N,D), mask:(B,N)
    m = mask.unsqueeze(-1).float()
    return (x*m).sum(1) / m.sum(1).clamp(min=1)

매 결정 기준

상황	Approach
Classification final feature	Global Avg Pooling
Variable input image	AdaptiveAvgPool2d
Edge-preserve detection	Max Pool 또는 strided conv
Transformer stage transition	Patch merging / strided conv
Set/sequence aggregation	Attention pool
Audio waveform	1D max/avg pool 또는 strided conv

기본값: feature map → GAP, downsample → strided conv (modern).

🔗 Graph

• 부모: Deep Learning
• 변형: Max_Pooling · Average_Pooling
• 응용: Image-Classification-Mastery · ResNet · ViT

🤖 LLM 활용

언제: CNN backbone에서 spatial reduce, classification head GAP, set/graph aggregation. 언제 X: dense prediction (segmentation, detection)에서 매 정보 손실 — skip connection 결합 또는 dilated conv 고려.

❌ 안티패턴

• Pool then upsample for segmentation without skip: 매 detail 손실. U-Net skip 사용.
• MaxPool everywhere in modern arch: 매 strided conv가 매 학습 가능 — 거의 dominant.
• Flatten without GAP: classification head fully-connected로 들어가면 매 huge params + overfit.
• Pool over tokens with [CLS] available: attention pool 또는 [CLS] readout 매 better.

🧪 검증 / 중복

• Verified (PyTorch docs nn.MaxPool2d, AdaptiveAvgPool, Swin Transformer paper, ConvNeXt paper).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — pooling types + modern shift to strided conv / attention pool