5 research outputs found
Heterogeneous Domain Adaptation via Soft Transfer Network
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in
a target domain by borrowing knowledge from a heterogeneous source domain. In
this paper, we propose a Soft Transfer Network (STN), which jointly learns a
domain-shared classifier and a domain-invariant subspace in an end-to-end
manner, for addressing the HDA problem. The proposed STN not only aligns the
discriminative directions of domains but also matches both the marginal and
conditional distributions across domains. To circumvent negative transfer, STN
aligns the conditional distributions by using the soft-label strategy of
unlabeled target data, which prevents the hard assignment of each unlabeled
target data to only one category that may be incorrect. Further, STN introduces
an adaptive coefficient to gradually increase the importance of the soft-labels
since they will become more and more accurate as the number of iterations
increases. We perform experiments on the transfer tasks of image-to-image,
text-to-image, and text-to-text. Experimental results testify that the STN
significantly outperforms several state-of-the-art approaches.Comment: Accepted by ACM Multimedia (ACM MM) 201
Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation
Partial domain adaptation (PDA) attracts appealing attention as it deals with
a realistic and challenging problem when the source domain label space
substitutes the target domain. Most conventional domain adaptation (DA) efforts
concentrate on learning domain-invariant features to mitigate the distribution
disparity across domains. However, it is crucial to alleviate the negative
influence caused by the irrelevant source domain categories explicitly for PDA.
In this work, we propose an Adaptively-Accumulated Knowledge Transfer framework
(AKT) to align the relevant categories across two domains for effective
domain adaptation. Specifically, an adaptively-accumulated mechanism is
explored to gradually filter out the most confident target samples and their
corresponding source categories, promoting positive transfer with more
knowledge across two domains. Moreover, a dual distinct classifier architecture
consisting of a prototype classifier and a multilayer perceptron classifier is
built to capture intrinsic data distribution knowledge across domains from
various perspectives. By maximizing the inter-class center-wise discrepancy and
minimizing the intra-class sample-wise compactness, the proposed model is able
to obtain more domain-invariant and task-specific discriminative
representations of the shared categories data. Comprehensive experiments on
several partial domain adaptation benchmarks demonstrate the effectiveness of
our proposed model, compared with the state-of-the-art PDA methods
Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation
Heterogeneous Domain Adaptation (HDA) addresses the transfer learning
problems where data from the source and target domains are of different
modalities (e.g., texts and images) or feature dimensions (e.g., features
extracted with different methods). It is useful for multi-modal data analysis.
Traditional domain adaptation algorithms assume that the representations of
source and target samples reside in the same feature space, hence are likely to
fail in solving the heterogeneous domain adaptation problem. Contemporary
state-of-the-art HDA approaches are usually composed of complex optimization
objectives for favourable performance and are therefore computationally
expensive and less generalizable. To address these issues, we propose a novel
Cross-Domain Structure Preserving Projection (CDSPP) algorithm for HDA. As an
extension of the classic LPP to heterogeneous domains, CDSPP aims to learn
domain-specific projections to map sample features from source and target
domains into a common subspace such that the class consistency is preserved and
data distributions are sufficiently aligned. CDSPP is simple and has
deterministic solutions by solving a generalized eigenvalue problem. It is
naturally suitable for supervised HDA but has also been extended for
semi-supervised HDA where the unlabelled target domain samples are available.
Extensive experiments have been conducted on commonly used benchmark datasets
(i.e. Office-Caltech, Multilingual Reuters Collection, NUS-WIDE-ImageNet) for
HDA as well as the Office-Home dataset firstly introduced for HDA by ourselves
due to its significantly larger number of classes than the existing ones (65 vs
10, 6 and 8). The experimental results of both supervised and semi-supervised
HDA demonstrate the superior performance of our proposed method against
contemporary state-of-the-art methods.Comment: Technical Repor
Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation
Heterogeneous domain adaptation (HDA) transfers knowledge across source and
target domains that present heterogeneities e.g., distinct domain distributions
and difference in feature type or dimension. Most previous HDA methods tackle
this problem through learning a domain-invariant feature subspace to reduce the
discrepancy between domains. However, the intrinsic semantic properties
contained in data are under-explored in such alignment strategy, which is also
indispensable to achieve promising adaptability. In this paper, we propose a
Simultaneous Semantic Alignment Network (SSAN) to simultaneously exploit
correlations among categories and align the centroids for each category across
domains. In particular, we propose an implicit semantic correlation loss to
transfer the correlation knowledge of source categorical prediction
distributions to target domain. Meanwhile, by leveraging target pseudo-labels,
a robust triplet-centroid alignment mechanism is explicitly applied to align
feature representations for each category. Notably, a pseudo-label refinement
procedure with geometric similarity involved is introduced to enhance the
target pseudo-label assignment accuracy. Comprehensive experiments on various
HDA tasks across text-to-image, image-to-image and text-to-text successfully
validate the superiority of our SSAN against state-of-the-art HDA methods. The
code is publicly available at https://github.com/BIT-DA/SSAN.Comment: Accepted at ACM MM 202
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare,
driven by the availability of data with multiple feature channels in rich-data
environments (e.g., intensive care units). However, in many other practical
situations, we can only access data with much fewer feature channels in a
poor-data environments (e.g., at home), which often results in predictive
models with poor performance. How can we boost the performance of models
learned from such poor-data environment by leveraging knowledge extracted from
existing models trained using rich data in a related environment? To address
this question, we develop a knowledge infusion framework named CHEER that can
succinctly summarize such rich model into transferable representations, which
can be incorporated into the poor model to improve its performance. The infused
model is analyzed theoretically and evaluated empirically on several datasets.
Our empirical results showed that CHEER outperformed baselines by 5.60% to
46.80% in terms of the macro-F1 score on multiple physiological datasets.Comment: Published in TKD