[SSDA][CLS] MARRS: Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation

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paper : https://openaccess.thecvf.com/content/CVPR2023W/ECV/papers/Jain_MARRS_Modern_Backbones_Assisted_Co-Training_for_Rapid_and_Robust_Semi-Supervised_CVPRW_2023_paper.pdf
CVPRW 2023 accepted (인용수: 0회, ‘23.10.30기준)
downsteram task : SSDA for Classification
Motivation
- SSDA for classification에 사용되던 baseline 모델 (Res-34)이 out-dated임
- 단순한 SSL기법 적용한 최신 모델을 활용해서 (CovNext-L) baseline모델 상회
Contribution
- SSDA task에 최신 CNN, Vistion-Transformer 모델을 활용한 Co-training 기반 backbone으로 교체 적용
- Diverse feature 학습을 위해 3가지 모듈 적용 (image-level, feature-level, backbone-level)
- SSDA Datazset SOTA 달성
- KD 활용한 Mbv2 모델로도 SSDA Dataset SOTA달성
Overview

Feature Extraction

Backbone level diversity Module (BD)
- CNN based : ConvNext-XL
- Vistion-Transformer based : Swin-L
Image level diversity Module (ID)
- training 시 weak-aug (psi)를 한쪽 모델에 주입
Feature distribution level diversity Module (FD)
- CORAL 을 한쪽 모델 출력에 연결

각 backbone의 confidence prediction을 pseudo-gt로 활용해서 학습 → Co-Training
Loss
- Supervised Loss : Labeled source db + Labeled target db (CE Loss)
  - n : D dataset에 속한 sample 갯수
  - K : Class 갯수
  - F : classifier F의 출력
  - Label smoothing을 통해 source에 over-confidence 방지
- Unsupervised Loss
  - Unlabeled target db (CELoss) : Co-training
  - Consistency regularization Loss : Strong-aug feature와 original feature간의 일치하도록 강제
    - Strong Aug = RandAugment 사용
KD
- Inference 속도 향상을 위해 적용
  - Teacher : Swin-L + CovNext-XL (MARRS)
  - Student : MobileNet-V2
Algorithm
Experiment
Ablation Studies
Inference Time