Pith. sign in

REVIEW 7 cited by

Frustratingly Easy Domain Adaptation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 0907.1815 v1 pith:A36RQ3JG submitted 2009-07-10 cs.LG cs.CL

classification cs.LGcs.CL
keywords adaptationdataapproachdomaineasyapproachesappropriatebetter
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms state-of-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multi-domain adaptation problem, where one has data from a variety of different domains.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning

    cs.LG 2025-05 conditional novelty 7.0 of 10

    COGNATE fine-tunes CPU-pretrained cost models on 500 accelerator samples, reaching 95% of optimal SpMM speedup on the SPADE simulator.

  2. Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks

    cs.CV 2019-06 unverdicted novelty 7.0 of 10

    SPN is a CNN that detects a spacecraft bounding box, classifies then regresses attitude, and optimizes position via Gauss-Newton, achieving degree-level attitude and cm-level position errors on real images after train...

  3. Transfer Learning Under High-Dimensional Network Convolutional Regression Model

    cs.LG 2025-04 conditional novelty 6.0 of 10

    Transfer learning is shown to improve estimation rates for high-dimensional lasso on network convolutional regression when informative source networks are available.

  4. FlexPose: Pose Distribution Adaptation with Limited Guidance

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A few-shot method that transfers a source pose distribution to a target one by fine-tuning only a few linear layers of a StyleGAN skeleton generator, with pose-mixup and sparse regularization.

  5. Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning

    stat.ML 2019-08 conditional novelty 5.0 of 10

    A population-aware hierarchical Bayesian domain adaptation model improves influenza prediction from symptoms on new datasets by sharing age and gender invariant components across environments.

  6. Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations

    cs.LG 2025-01 conditional novelty 4.0 of 10

    Transfer learning of Gaussian process surrogates is improved by fitting nonlinear beta-CDF input warping plus affine transformations on small target datasets.

  7. Tackling Online Abuse: A Survey of Automated Abuse Detection Methods

    cs.CL 2019-08 accept novelty 2.0 of 10

    Automated abuse detection is mapped through datasets and methods, with character-level features and user profiling as key trends and implicit abuse as the main unsolved challenge.

Pith tools