MixMatch - What is the reason for only a single lambda for all mixing samples?

[barskern]

TorchSSL/models/mixmatch/mixmatch_utils.py

Lines 23 to 37 in b45c3b3

	def mixup_one_target(x, y, gpu, alpha=1.0, is_bias=False):
	"""Returns mixed inputs, mixed targets, and lambda
	"""
	if alpha > 0:
	lam = np.random.beta(alpha, alpha)
	else:
	lam = 1
	if is_bias:
	lam = max(lam, 1 - lam)
	index = torch.randperm(x.size(0)).cuda(gpu)
	mixed_x = lam * x + (1 - lam) * x[index]
	mixed_y = lam * y + (1 - lam) * y[index]
	return mixed_x, mixed_y, lam

In the code snippet above, a single lam parameter is selected for all mix-up operations. In google's implementation of MixMatch they sample a beta distribution individually for each sample (see code below). Is there a reason for the choice to only sample the beta distribution once here?

https://github.com/google-research/mixmatch/blob/1011a1d51eaa9ca6f5dba02096a848d1fe3fc38e/libml/layers.py#L166-L175