Consistent Assistant Domains Transformer for Source-Free Domain Adaptation
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces Consistent Assistant Domains Transformer (CADTrans) for source-free domain adaptation, enhancing feature consistency and diversity. CADTrans effectively addresses domain bias and hard samples, improving model performance without source data access.
Area Of Science
- Computer Science
- Artificial Intelligence
- Machine Learning
Background
- Source-free domain adaptation (SFDA) faces challenges due to inaccessible source data, hindering the extraction of deterministic invariable features.
- Existing SFDA methods often struggle with hard samples and domain bias by focusing on target domain features resembling the source domain.
Purpose Of The Study
- To propose a novel method, Consistent Assistant Domains Transformer (CADTrans), for effective SFDA.
- To address limitations in feature representation diversity and improve robustness against domain bias and hard samples in SFDA.
Main Methods
- Developed an assistant domain module within CADTrans to generate diversified feature representations from aggregated global attentions.
- Employed multiple consistent strategies using assistant and target domains to obtain invariable feature representations for distinguishing easy and hard samples.
- Introduced a conditional multi-kernel max mean discrepancy (CMK-MMD) strategy to align hard samples with easy samples by differentiating between intra-class and inter-class samples.
Main Results
- CADTrans demonstrates significant performance improvements across various benchmarks, including Office-31, Office-Home, VISDA-C, and DomainNet-126.
- The proposed methods effectively construct invariable feature representations with domain consistency.
- The approach successfully distinguishes and aligns hard samples, mitigating issues of domain bias.
Conclusions
- CADTrans offers a robust solution for SFDA by enhancing feature consistency and diversity.
- The method shows superior performance compared to existing approaches on standard SFDA benchmarks.
- The developed techniques provide a promising direction for future research in domain adaptation without source data.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
In order to determine the unknown voltage for a circuit composed of a current source and a collection of resistors, capacitors, and inductors - each with their...
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...