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Incremental Adversarial Domain Adaptation for Continually Changing Environments
Markus Wulfmeier, Alex Bewley, Ingmar Posner
TL;DR
Appearance shifts challenge deployed models, and existing domain adaptation does not exploit continuity in those shifts or avoid the burden of retaining source data. IADA incrementally adapts across changing target domains and can use a GAN approximation of source features; it outperforms union-target regular adaptation in the final domain, while its source-data-independent variant has only minor performance reductions.
Problem
Continuous environmental shifts challenge deployed models, while unsupervised domain adaptation does not exploit their temporal continuity and can require retaining large source datasets.
Source: §I · p. 1Method
IADA incrementally adapts to changing target domains and can use a GAN-generated approximation of source features instead of retained source data.
Source: §I · pp. 1–2Results
IADA significantly outperforms regular adaptation using the union of target domains in the final domain, while source-domain-modeling variants incur only minor performance reductions.
Source: A. Toy Example: Incrementally… · p. 5, §V · p. 6Takeaways & Limitations
The source-data-independent deployment variant has only minor performance reduction and is intended for mobile platforms with limited memory.
Source: §I · p. 1IADA relies on access to incremental appearance shifts; with limited access or step-wise changes, it degrades to regular adversarial domain adaptation.
Source: §V · p. 6
Abstract
from arXiv · showhide
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance.
I. INTRODUCTION
Appearance shifts can degrade deployed models, while large changes such as day to night remain challenging for domain adaptation. IADA exploits gradual changes during deployment to adapt incrementally, and a source-feature GAN reduces the need to retain source data.
I. INTRODUCTION
- Appearance shifts from lighting, seasons, and weather can move outdoor-robot perception inputs away from training data and reduce deployment accuracy.Large changes such as day and night remain a substantial challenge despite prior adversarial adaptation results.Source: §I · p. 1
- IADA incrementally updates the encoder and discriminator as environmental appearance shifts, using intermediate domains to address larger source-to-target differences.The approach exploits gradual changes observed during deployment rather than aligning source and target in one step.Source: §I · p. 1, Figure 1 · p. 1
- A GAN approximates the source-domain feature distribution, making adaptation independent of retained source-data volume with only minor performance reduction.This is intended to address storage constraints on mobile platforms.Source: §I · pp. 1–2
- The paper evaluates incremental adaptation on synthetic shifts and real-world drivable-path segmentation across illumination conditions in the Oxford RobotCar Dataset.The introduction also identifies online application feasibility on an NVIDIA GPU as part of the contribution.Source: §I · p. 2
II. RELATED WORK
Prior work addresses domain appearance shifts through feature alignment, retained experiences, and image synthesis, but it is unclear how these approaches scale to continuous shifts. Most unsupervised adaptation methods treat domain change as a batch transition rather than exploiting temporal coherence.
II. RELATED WORK
- Prior approaches use feature transformations, retained experiences, or synthesized images, but their efficient scaling to continuous domain shifts remains unclear.Deep-network approaches also address appearance changes through feature representations and domain adaptation.Source: §II · p. 2
- Feature-alignment methods include matching multiple-layer distributions, penalizing cross-domain parameter deviations, aligning second-order statistics, and matching source and predicted-target label statistics.The cited examples are Long et al., Rozantsev et al. [17], Sun et al., and Hoffman et al. [9], respectively.Source: §II · p. 2
- Adversarial adaptation trains a domain discriminator on encoder features, but its optimization can be unstable, motivating confusion-loss and Wasserstein-GAN approaches.The cited approaches include, [4],, confusion loss, and Wasserstein GAN.Source: §II · p. 2
- Most prior unsupervised adaptation treats domain change as a batch transition, whereas this work extends adversarial adaptation to evolving targets using robots’ ongoing observations.Hoffman et al. is identified as an exception that uses predefined features to address a continuously reshaping target domain.Source: §II · p. 2
III. METHOD
IADA applies adversarial domain adaptation to a stream of changing target domains, gradually updating the target encoder and discriminator. It keeps the source encoder and supervised module fixed, and can replace source feature examples with samples from a fixed GAN.
III. METHOD
- The approach targets continuous environmental changes such as weather and lighting, where incremental shifts accumulate into larger domain differences.Dividing alignment into smaller shifts is presented as a way to simplify the overall task and potentially minimize loss of relevant information.Source: §III · pp. 2–3
- IADA treats incoming data as incrementally changing domains and gradually updates the encoder and discriminator to align across larger overall shifts.Previously adapted parameters initialize adaptation to the currently encountered domain, using a continuously sampled buffer of unsupervised data.Source: §III · pp. 2–3
- The target encoder is trained to confuse a domain discriminator, producing domain-invariant features that can be processed by the source-trained supervised module.At deployment, the target encoder connects to the supervised module.Source: §III · p. 2, Figure 2 · p. 3
- The source encoder and supervised module remain fixed during adaptation, while only the target encoder and discriminator are trained.Keeping source-side parameters unchanged preserves source-domain performance.Source: §III · p. 3
- A source-feature GAN is trained offline, then held fixed while the target encoder learns to mimic its feature distribution.The GAN approximates source features without propagating adversarial-loss gradients to the source encoder.Source: Figure 3 · p. 3
A. Source Distribution Modelling
To reduce the source-data storage burden, the method models source-domain features with a GAN and aligns the target encoder to that model during adaptation. The GAN remains static during adaptation, making the procedure independent of the amount of source data.
A. Source Distribution Modelling
- GAN-based source modelling removes the need to retain source training data during adaptation, making the method independent of source-dataset size.The paper frames this as addressing storage constraints on continuously deployed platforms.Source: A. Source Distribution Modelling · p. 3
- During offline training, generator G maps normally distributed noise to an approximation of the source-domain feature distribution, not natural images.The generated features are denoted fg = G(z, θG).Source: A. Source Distribution Modelling · pp. 3–4
- During adaptation, the target encoder aligns to the static GAN feature distribution while the discriminator distinguishes generated source features from actual target features.The target encoder and discriminator use the objectives in Equations 5 and 6.Source: A. Source Distribution Modelling · p. 4
- For SDM, source-trained models initialize target-domain training, including the discriminator; the deployment setup otherwise matches IADA.Source: A. Source Distribution Modelling · p. 4
IV. EXPERIMENTS
The experiments cover an artificially controlled toy domain shift and real-world drivable-path segmentation. They compare incremental adaptation with one-step adaptation and examine source-domain modelling.
IV. EXPERIMENTS
- Evaluation has two parts: an artificially induced domain-shift toy scenario and a continuous-deployment drivable-path segmentation task for autonomous mobility.Source: §IV · p. 4
- The comparison tests IADA against one-step ADA and examines source-domain modelling; metrics are classification accuracy for the toy task and mean average precision for path segmentation.Source: §IV · p. 4
- ADA uses only the final target domain, IADA uses all incremental domains, and ADA Union applies regular ADA to their combined dataset.ADA Union tests whether IADA's advantage is simply due to using a larger dataset.Source: §IV · p. 4
A. Toy Example: Incrementally Transformed MNIST
On the incrementally transformed MNIST task, IADA improves final-target accuracy over one-step ADA, while source-domain modelling has only a minor performance cost. The benefits of adding intermediate domains saturate around 10–20 for the tested rescaling, though more complex transformations may require more.
A. Toy Example: Incrementally Transformed MNIST
- The synthetic setup uses height-rescaled copies of MNIST to control domain-shift strength and make changes observable for adaptation.The transformed digits range from 0.9 to 0.5 times their original height; Figure 4 shows source data above and the maximally transformed target below, with five intermediate domains.Source: A. Toy Example: Incrementally… · p. 4, Figure 4 · p. 4
- IADA significantly improves final MNIST accuracy over one-step ADA; ADA Union helps only slightly, while IADA with SDM remains above the ADA baseline despite minor performance reductions.Source: Table I · p. 4, A. Toy Example: Incrementally… · pp. 4–5
- The table compares one-step and incremental methods, with and without GAN-based Source Domain Modelling, on target-domain classification accuracy.Source: A. Toy Example: Incrementally… · p. 4
- For MNIST rescaled to 0.3 of its original height, accuracy benefits from more intermediate domains up to around 10–20, after which further increases do not significantly change target performance.The authors note that more complex transformations may benefit from still more incremental domains.Source: Figure 5 · p. 5, A. Toy Example: Incrementally… · p. 5
B. Free Space Segmentation
The segmentation evaluation uses morning-labeled source data and five lighting-shifted target domains, then compares incremental adaptation with one-step alternatives. The final target includes an abrupt streetlight change, while source-domain modeling slightly reduces performance and enables faster adaptation without retaining large source datasets.
B. Free Space Segmentation
- The real-world task is drivable-path segmentation, motivated by the need to detect traversable terrain across deployment domains and weather conditions.The segmentation architecture is ENet-based and selected to balance accuracy and computational requirements for mobile deployment.Source: B. Free Space Segmentation · p. 5
- The evaluation uses five intermediate lighting domains from morning to evening, with about 2,000 images per domain and labels available for training only in the morning source domain.The evaluation images are rescaled to 128 by 320 pixels; test labels are used to evaluate all domains.Source: B. Free Space Segmentation · p. 5, Figure 6 · p. 5
- IADA outperforms one-step adaptation on drivable-path segmentation; adaptation over the union of target domains is more accurate than on the final domain alone, but less accurate than IADA.The final domain’s streetlight switch-on creates an instantaneous lighting change from previous target domains, making union-based adaptation less efficient.Source: B. Free Space Segmentation · pp. 5–6, Table II · p. 6
- More continuous appearance changes are expected to increase IADA’s advantage; the streetlight switch-on instead creates a step change in the final target domain.The reported target domains approximate continuous application using distinct offline datasets.Source: B. Free Space Segmentation · pp. 5–6
- Approximately 26 minutes per new target domain supports about 55 daily updates; source-domain modeling takes 29 minutes and supports nearly 50 updates per day.Both timings are reported for an NVIDIA GeForce GTX Titan Xp GPU.Source: B. Free Space Segmentation · p. 6
V. DISCUSSION
IADA’s benefits depend on observing incremental environmental shifts, while the segmentation evaluation uses distinct offline domains and includes an abrupt lighting change. The discussion also weighs adaptation speed and storage against performance and compute trade-offs.
V. DISCUSSION
- The segmentation evaluation uses distinct offline target domains, though sliding-window sampling could extend the method to more continuous online alignment.The synthetic evaluation found that benefits can saturate as intermediate domains become increasingly similar.Source: §V · p. 6
- IADA relies on access to incremental appearance shifts; when shifts are limited or step-wise, it degrades to regular adversarial domain adaptation.The segmentation setting’s streetlight switch-on is an example of an instantaneous appearance change.Source: §V · p. 6
- IADA significantly outperforms adaptation over the union of target domains under the final domain’s instantaneous lighting shift, by optimizing for the currently relevant target.The streetlight change makes the final domain differ sharply from previous target domains.Source: §V · p. 6
- The confusion-loss formulation was chosen over Wasserstein GAN adaptation because it trains significantly faster; WGAN training is slightly more stable with statistically insignificant performance improvement.WGAN requires additional critic training rounds.Source: §V · p. 6
- The minor accuracy loss from source-domain approximation can be avoided with the original IADA formulation when cloud computing or larger storage is available.Increased computational effort may limit embedded-system applications.Source: §V · p. 6
VI. CONCLUSION AND FUTURE WORK
The paper presents incremental domain adaptation and GAN-based source feature modeling, evaluating them on synthetic shifts and real-world drivable-path segmentation. It identifies security risks and catastrophic forgetting as future-work challenges for continual training during deployment.
VI. CONCLUSION AND FUTURE WORK
- The paper presents incremental adaptation to a continuous stream of target domains and GAN-based modeling of source features to avoid retaining large source datasets during adaptation.The methods are evaluated on synthetically shifted MNIST digits and real-world autonomous-driving drivable-path segmentation.Source: §VI · p. 6
- Continual training during deployment raises security concerns because adversarial samples could distort a model beyond its current prediction.The paper identifies defense against adversarial examples as an essential direction for further research.Source: §VI · p. 6
- Addressing catastrophic forgetting is a future-work direction that could reduce computation by avoiding readaptation to previously encountered target domains.Source: §VI · p. 6