Aneeshan Sain

The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variations and higher intra-class variations). To alleviate this problem, prior works predominately use a support set to reconstruct the query image and then utilize metric learning to determine its category. Upon careful inspection, we further reveal that such unidirectional reconstruction methods only help to increase inter-class variations and are not effective in tackling intra-class variations. In this paper, we for the first time introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. In addition to using the support set to reconstruct the query set for increasing inter-class variations, we further use the query set to reconstruct the support set for reducing intra-class variations. This design effectively helps the model to explore more subtle and discriminative features which is key for the fine-grained problem in hand. Furthermore, we also construct a self-reconstruction module to work alongside the bi-directional module to make the features even more discriminative. Experimental results on three widely used fine-grained image classification datasets consistently show considerable improvements compared with other methods. Codes are available at: https://github.com/PRIS-CV/Bi-FRN.

Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled
target domain. Amongst its many variants, open set domain adaptation (OSDA) is perhaps the most challenging one, as it further
assumes the presence of unknown classes in the target domain. In this paper, we study OSDA with a particular focus on enriching its
ability to traverse across larger domain gaps, and we show that existing state-of-the-art methods suffer a considerable performance
drop in the presence of larger domain gaps, especially on a new dataset (PACS) that we re-purposed for OSDA. Exploring this is
pivotal for OSDA as with increasing domain shift, identifying unknown samples in the target domain becomes harder for the model, thus
making negative transfer between source and target domains more challenging. Accordingly, we propose a Mutual-to-Separate (MTS)
framework to address the larger domain gaps. Essentially we design two networks – (a) Sample Separation Network (SSN): which is
trained to learn a hyperplane for separating unknown samples from known ones, and (b) Distribution Matching Network (DMN): which is
trained to maximise domain confusion between source and target domains without unknown samples under the guidance of the SSN.
The key insight lies in how we exploit the mutually beneficial information between these two networks. On closer observation, we see
that SSN can reveal which samples in the target domain belong to the unknown class by instance weighting whereas, DMN pushes apart
the samples that most likely belong to the unknown class in the target domain, which in turn reduces the difficulty of SSN in identifying
unknown samples. It follows that (a) and (b) will mutually supervise each other and alternate until convergence, which can better align the
source and target domains in the shared label space. Extensive experiments on five datasets (Office-31, Office-Home, PACS, VisDA, and
mini DomainNet) demonstrate the efficiency of the proposed method. Detailed ablation experiments also validate the effectiveness of
each component and the generality of the proposed framework. Codes are available at: https://github.com/PRIS-CV/Mutual-to-Separate.