Energy Ring: Stabilizing Feature Energy for Reliable Test-Time Discovery
Authors: Tianle Liu, Fan Lyu, Tingliang Feng, Chenggong Ni, Tianqi Wang, Zhang Zhang, Fuyuan Hu, Rui Yao, Tao Zhou
Organizations: School of Electronics and Information Engineering, Suzhou University of Science and Technology · Computer Vision Center, Universitat Autònoma de Barcelona · College of Computer Science, Northwest University · School of Intelligence Science and Technology, Nanjing University · New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences · School of Computer Science and Technology, China Unviersity of Mining and Technology · School of Computer Science Engineering, North Minzu University
Test-Time Discovery (TTD) aims to recognize known classes and detect novel ones while the model is already deployed, operating directly on a continual unlabeled test-time stream. Existing TTD methods are overly conservative, making known and unknown features collapse together and difficult to separate during test-time adaptation. In this paper, we revisit TTD from an energy perspective and find that the natural energy gap between known and unknown classes is unstable and easily collapses. We therefore propose Energy Ring to stabilize this gap by anchoring the two types of classes on separate energy rings. A lightweight adapter enlarges the norm-based gap while preserving known-class directions, and a perturbation consistency module forms compact clusters for unknown classes. During testing, energy-guided confidence weighting improves prediction, and a stability-based rectification mechanism corrects unreliable updates. Experimental results show that Energy Ring significantly improves TTD performance against existing TTD methods.
Figures & tables
Figure 1: Energy comparisons among existing TTD methods and our method. HM keeps the backbone frozen and classifies based on fixed spatial energy. DAA collapses the original energy intensity onto a single hypersphere. Energy Ring compresses known and unknown classes onto different energy rings, yielding spatial-energy difference and retaining separability.
Figure 2: Task setting of TTD. In TTD, a model is pretrained on data containing only known classes. During deployment, the test data may include both known and unknown classes, requiring the model to predict known classes and discover novel ones.
Figure 3: Comparison of norm and angular prediction for test samples. Our method further expands the differences of energy in both intensity and direction.
Figure 4: Method Schema. ERA projects features onto concentric energy rings: high-energy ring for known classes and low-energy ring for unknowns, together with perturbation-consistency during training. During testing, we conduct energy-weighted prediction, while selectively rectifying high-perturbation samples to refresh memory queues and update ERA to rectify energy rings.
Method
Real-time Evaluation
Post Evaluation
KA ↑
TA ↑
CA ↑
KA ↑
TA ↑
CA ↑
KF ↓
CIFAR100D
L2P ( Wang et al., 2022b )
59.93±2.15
8.57±2.49
43.10±4.30
50.53±7.25
9.60±1.50
27.39±2.11
27.85±7.21
DP ( Wang et al., 2022a )
66.09±1.01
8.80±1.69
48.34 ±6.78
56.19±2.00
8.68±2.06
28.93±2.25
29.06±1.99
GMP ( Cendra et al., 2024 )
72.77±1.20
7.37±0.88
42.26±4.54
67.21±2.53
13.04±2.59
27.10±1.77
17.69±2.53
PHE ( Zheng et al., 2024 )
72.35±1.12
16.74±1.05
37.34±1.31
70.12±1.07
14.55± 1.01
24.65±1.35
2.12 ±0.92
HM ( Lyu et al., 2025b )
79.17± 0.13
21.13± 0.62
56.37 ±1.42
80.73 ±1.59
31.03±1.24
34.81± 1.22
3.41 ±1.49
Table 1: Comparisons on four datasets. Real-time evaluation reflects the accumulated performance across all test batches, while post evaluation reassesses all test samples after testing. Bold and underlining denote the best and second-best results.
Method
CIFAR100D
CUB200D
T-ImageNetD
AircraftD
HCA
ARI
NMI
VM
HCA
ARI
NMI
VM
HCA
ARI
NMI
VM
HCA
ARI
NMI
VM
HM ( Lyu et al., 2025b )
63.69
48.35
73.08
73.08
50.46
27.81
70.25
70.25
56.64
40.35
72.70
72.70
33.30
21.33
46.46
46.46
DAA ( Liu et al., 2025 )
65.62
53.96
75.20
75.21
58.19
32.43
75.29
75.29
58.91
42.50
73.53
73.53
32.08
21.20
46.44
46.44
Ours
66.38
53.54
75.36
75.36
58.34
33.70
75.53
75.53
58.25
43.26
73.36
73.36
33.21
22.13
47.02
47.02
Table 2: Comparisons of clustering metrics of post evaluation on four datasets.
Figure 5: Comparison on balance of Real-time TA and CA. Perfect Balance represents the most balanced TA and CA (TA = CA).
Table 8Figure 9
Figure 8: T-SNE visualization in angular space on CIFAR100D with 5 known and 5 unknown classes.
Figure 9: Matching comparison between unknown class predictions and ground truth across methods on CIFAR-100D (90+10). Black rectangles indicate old class samples misclassified as unknowns.
Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data. While achieving optimal accuracy, existing CTTA approaches present poor real-world applicability on resource-constrained edge devices, due to the substantial memory overhead and energy consumption. In this work, we first introduce a novel paradigm -- on-demand TTA -- which triggers adaptation only when a significant domain shift is detected. Then, we present OD-TTA, an on-demand TTA framework for accurate and efficient adaptation on edge devices. OD-TTA comprises three innovative techniques: 1) a lightweight domain shift detection mechanism to activate TTA only when it is needed, drastically reducing the overall computation overhead, 2) a source domain selection module that chooses an appropriate source model for adaptation, ensuring high and robust accuracy, 3) a decoupled Batch Normalization (BN) update scheme to enable memory-efficient adaptation with small batch sizes. Extensive experiments show that OD-TTA achieves comparable and even better performance while reducing the energy and computation overhead remarkably, making TTA a practical reality.
Xiao Ma, Young D. Kwon, Dong Ma
Singapore Management University, Singapore · Samsung AI Center–Cambridge, United Kingdom · University of Cambridge, United Kingdom
Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference. However, these updates raise the issue of privacy w.r.t. the testing data, because the model parameters now depend on all past inputs. To control this privacy risk, we cast multiple popular TTA methods (Tent, EATA, SAR, DeYO, and COME) into differential privacy (DP) forms that apply per-sample gradient clipping and Gaussian noise for all updates. On ImageNet-C, our DP-TTA methods provide adequate privacy at small cost to accuracy, and in the low-privacy regime the clipping mechanism of DP can even improve the accuracy and stability of adaptation in the continual setting. These improvements to privacy and accuracy come at only modest computational overhead. These first results on private TTA raise awareness of the issue, inform the development of more private test-time updates, and identify per-sample clipping as an effective technique for improving the accuracy and stability of adaptation.
This paper studies continual test-time adaptation (CTTA), the task of adapting a model to constantly changing unseen domains in testing while preserving previously learned knowledge. Existing CTTA methods mostly focus on adaptation to the current test domain only, overlooking generalization to arbitrary test domains a model may face in the future. To tackle this limitation, we present a novel online test-time domain generalization framework for CTTA, dubbed TestDG. TestDG aims to learn features invariant to both current and previous test domains on the fly during testing, improving the potential for effective generalization to future domains. To this end, we propose a new model architecture and a test-time adaptation strategy dedicated to learning domain-invariant features, along with prototype selection and update mechanisms for effectively managing information from previous test domains. TestDG achieved state of the art on three of four public CTTA benchmarks and performed on par with the best prior method on ImageNet-C. Moreover, it showed superior generalization to unseen test domains.