7 research outputs found
Ranking Neural Checkpoints
This paper is concerned with ranking many pre-trained deep neural networks
(DNNs), called checkpoints, for the transfer learning to a downstream task.
Thanks to the broad use of DNNs, we may easily collect hundreds of checkpoints
from various sources. Which of them transfers the best to our downstream task
of interest? Striving to answer this question thoroughly, we establish a neural
checkpoint ranking benchmark (NeuCRaB) and study some intuitive ranking
measures. These measures are generic, applying to the checkpoints of different
output types without knowing how the checkpoints are pre-trained on which
dataset. They also incur low computation cost, making them practically
meaningful. Our results suggest that the linear separability of the features
extracted by the checkpoints is a strong indicator of transferability. We also
arrive at a new ranking measure, NLEEP, which gives rise to the best
performance in the experiments.Comment: Accepted to CVPR 202
Demystifying Assumptions in Learning to Discover Novel Classes
In learning to discover novel classes (L2DNC), we are given labeled data from
seen classes and unlabeled data from unseen classes, and we train clustering
models for the unseen classes. However, the rigorous definition of L2DNC is
unexplored, which results in that its implicit assumptions are still unclear.
In this paper, we demystify assumptions behind L2DNC and find that high-level
semantic features should be shared among the seen and unseen classes. This
naturally motivates us to link L2DNC to meta-learning that has exactly the same
assumption as L2DNC. Based on this finding, L2DNC is not only theoretically
solvable, but can also be empirically solved by meta-learning algorithms after
slight modifications. This L2DNC methodology significantly reduces the amount
of unlabeled data needed for training and makes it more practical, as
demonstrated in experiments. The use of very limited data is also justified by
the application scenario of L2DNC: since it is unnatural to label only
seen-class data, L2DNC is sampling instead of labeling in causality. Therefore,
unseen-class data should be collected on the way of collecting seen-class data,
which is why they are novel and first need to be clustered
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
In few-shot domain adaptation (FDA), classifiers for the target domain are
trained with accessible labeled data in the source domain (SD) and few labeled
data in the target domain (TD). However, data usually contain private
information in the current era, e.g., data distributed on personal phones.
Thus, the private information will be leaked if we directly access data in SD
to train a target-domain classifier (required by FDA methods). In this paper,
to thoroughly prevent the privacy leakage in SD, we consider a very challenging
problem setting, where the classifier for the TD has to be trained using few
labeled target data and a well-trained SD classifier, named few-shot hypothesis
adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private
information in SD will be protected well. To this end, we propose a target
orientated hypothesis adaptation network (TOHAN) to solve the FHA problem,
where we generate highly-compatible unlabeled data (i.e., an intermediate
domain) to help train a target-domain classifier. TOHAN maintains two deep
networks simultaneously, where one focuses on learning an intermediate domain
and the other takes care of the intermediate-to-target distributional
adaptation and the target-risk minimization. Experimental results show that
TOHAN outperforms competitive baselines significantly