Results and Discussion
Overall performance. Table 1 compares the MRAE values to
the noise-free trained model between ConFrag and the baselines.
We evaluate six types of noise: four symmetric and two random Gaussian noises.
ConFrag and Co-ConFrag achieve the strongest performance in all experiments compared to the fourteen baselines.
Notably, Co-ConFrag mixes co-teaching during the regression learning phase by assuming that \mathcal{S} still contains 25% noise.
The results on UTKFace-B dataset can be found in Appendix G.1 .

Selection/ERR/MRAE comparison. Fig. 5 compares ConFrag to five selection and refurbishment baselines of CNLCU-H, BMM, DY-S, AUX, Selfie on IMDB-Clean-B using the selection rate, ERR, and MRAE.
Ideally, a model should attain a high selection rate and a low ERR.
It is worth noting that the relative importance of ERR and selection rate may vary depending on the dataset and the task.
ConFrag achieves the lowest ERR while maintaining above-average selection rates, resulting in the best MRAE.
Appendix G.10 includes comparison results for all noise types with more baselines.

Fragment pairing. Fig. 6 (a) compares contrastive pairing to alternative pairings
using MRAE as a metric. The contrastive fragment pairing demonstrates superior performance to other pairing methods.
Notably, the performance is poorest when both the average and minimum distance between fragments are smallest ( [1,2],[3,4] when F=4 , [1,2],[3,4],[5,6] when F=6 ).
While the pairings of [1,4],[2,3] and [1,6],[2,5],[3,4] have the same average distance between fragments as the contrastive pairings, their minimum distances between fragments are smaller, resulting in poorer performances than contrastive pairings.
This result shows the effectiveness of contrastive fragment pairing for selecting clean samples.
See Appendix G.4 for more details.

Fragment number. ConFrag introduces a hyperparameter F , the number of fragments.
While we simply set F=4 for all experiments, we conduct analysis on the effect of using different F , as shown in Fig. 6 (b).
On SHIFT15M-B dataset, the performance is relatively stable across different fragment numbers.
On IMDB-Clean-B, a small declining trend in performance is observed as the number of fragments increases.
This decrease is likely attributed to a finer division of the training data among feature extractors, ultimately leading to overfitting and reduced generalization capabilities.
Appendix G.2 provides further analysis of the fragment number.

Ablation analysis on mixture of neighboring fragments. In Table 3 , we conduct an ablation analysis of the Mixture of neighboring fragments (§ 2.3 ).
When evaluating neighborhood agreement based solely on either the agreement of the current fragment ( \alpha_{f}^{\text{self}} ) or the neighboring fragment’s agreement ( \alpha_{f}^{\text{ngb}} ),
the ablation reveals that relying on the current fragment’s agreement alone ( \alpha_{f}^{\text{self}} ) exhibited relatively stronger performance.
Nevertheless, this approach still fell short of achieving a satisfactory level compared to considering both agreements, as defined in Eq. 4 .

Next, as we consider sample selection based on two variants of agreements, the predictive one utilizing the feature extractor’s binary classifier and the representational one using K -nearest neighbors on the learned feature space
(referred to as the selected sample sets \mathcal{S}^{p} and \mathcal{S}^{r} respectively), we conduct an ablation study on these selected sample sets.
This involves evaluating the results when determining the final selected sample set ( \mathcal{S} ) either individually, at the intersection, or at the union of \mathcal{S}^{p} and \mathcal{S}^{r} .
Overall, in line with ConFrag, the union of sets ( \mathcal{S}^{p}\cup\mathcal{S}^{r} ) proves to be the most effective strategy.

Parameter size comparison. Table 3 compares the number of parameters of ConFrag and baselines on the ResNet-based age prediction datasets. A thorough description of the ConFrag architecture is in Appendix F.3 .
It is worth noting that each of the ConFrag’s feature extractors for noise mitigation employs a much fewer number of parameters than the downstream regression task ( e.g ., 48% in age prediction datasets). The total number of parameters of each method varies, as some share parameters for regression as well as noise mitigation while others, such as ConFrag, do not.
Nevertheless, ConFrag uses fewer total parameters than CNLCU-H and RDI.