Table 4. Ablation study on different components of DiRA:

We study the impact of each component of DiRA, including discrimination, restoration, and adversary, in four downstream tasks. Adding restorative learning $\left({\mathcal{L}}_{\mathit{\text{res}}}\right)$ to discriminative learning leads to consistent performance improvements. Furthermore, equipping models with adversarial learning $\left({\mathcal{L}}_{\mathit{\text{adv}}}\right)$ yields performance boosts across all tasks.

Base	Pretraining dataset	${\mathcal{L}}_{\mathit{\text{dis}}}$	${\mathcal{L}}_{\mathit{\text{res}}}$	${\mathcal{L}}_{\mathit{\text{adv}}}$	Classification [AUC (%)]		Segmentation [Dice (%)]
					ChestX-ray14	CheXpert	SIIM-ACR	Montgomery
		✓	×	×	80.36 ±0.26	86.42±0.42	67.89±1.14	98.03±0.22
MoCo-v2	ChestX-ray14	✓	✓	×	80.72±0.29 ↑	86.86 ±0.37 ↑	68.16± 1.07 ↑	98.19±0.08 ↑
		✓	✓	✓	81.12±0.17 ↑	87.59±0.28 ↑	69.24±0.41 ↑	98.24±0.09 ↑
		✓	×	×	80.45±0.29	86.90 ±0.62	69.71±0.34	98.13±0.13
Barlow Twins	ChestX-ray14	✓	✓	×	80.86 ±0.16 ↑	87.44±0.33 ↑	69.83±0.29 ↑	98.15±0.14 ↑
		✓	✓	✓	80.88±0.30 ↑	87.50±0.27 ↑	69.87±0.68 ↑	98.16±0.06 ↑
		✓	×	×	79.62±0.34	83.82±0.94	67.58±1.89	97.72±0.27
SimSiam	ChestX-ray14	✓	✓	×	79.41 ±0.42 ↑	84.45±0.46 ↑	68.35±1.16 ↑	98.02±0.21 ↑
		✓	✓	✓	80.44±0.29 ↑	86.04±0.43 ↑	68.76±0.69 ↑	98.17±0.11 ↑