Overcoming Label Noise for Source-free Unsupervised Video Domain
  Adaptation
Avijit Dasgupta C. V. Jawahar Karteek Alahari
Despite the progress seen in classification methods, current approaches for
handling videos with distribution shifts in source and target domains remain
source-dependent as they require access to the source data during the
adaptation stage. In this paper, we present a self-training based source-free
video domain adaptation approach to address this challenge by bridging the gap
between the source and the target domains. We use the source pre-trained model
to generate pseudo-labels for the target domain samples, which are inevitably
noisy. Thus, we treat the problem of source-free video domain adaptation as
learning from noisy labels and argue that the samples with correct
pseudo-labels can help us in adaptation. To this end, we leverage the
cross-entropy loss as an indicator of the correctness of the pseudo-labels and
use the resulting small-loss samples from the target domain for fine-tuning the
model. We further enhance the adaptation performance by implementing a
teacher-student framework, in which the teacher, which is updated gradually,
produces reliable pseudo-labels. Meanwhile, the student undergoes fine-tuning
on the target domain videos using these generated pseudo-labels to improve its
performance. Extensive experimental evaluations show that our methods, termed
as CleanAdapt, CleanAdapt + TS, achieve state-of-the-art results, outperforming
the existing approaches on various open datasets. Our source code is publicly
available at https://avijit9.github.io/CleanAdapt.