# BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition
## 4 Experiments
### Long-Tailed Image Recognition  
**Comparison Methods. ** Considering the rapid development of long-tailed image recognition, we have compared it with multiple related methods, including the method of modifying the loss function in one stage and the transfer strategy method in two stages. For better comparison, the method we chose and our commonality is that we did not add feature enhancement methods such as Mixup. For example, BS [32] for loss reweighting, NCM [2], \(\tau\)-Norm [2], and cRT [2] split the training process into two stages: classifier and representation learning, while LFME [51] used transfer learning, etc. Tables 3 and 4, respectively, list the test results for the large-scale ImageNet-LT dataset and the different IFs distributions CIFAR100-LT datasets. **Results. ** Dividing the long-tailed image recognition task into two stages and transferring the model weights from the first stage to the second stage can effectively improve the performance of long-tailed image recognition and outperform many existing methods. This conclusion can be drawn from ImageNet-LT in Table 3. Our weights transfer achieved 44.6% using the naive model with normal CE loss, which is superior to the naive model with an accuracy of 39.6%. It even outperforms other multi-stage learning methods including OLTR (35.6%) [34] and NCM (44.3%) [2]. By learning the CE classifier to regularize with weight decay, we improve accuracy from 44.6% to 46.4%. Using the weight decay to regularize the balanced loss classifier, the accuracy of the Few classes is improved by 13-16%. The accuracy of the Many classes in CBW is better than that in BSW, but the accuracy of the Few classes is lower than that in BSW. Therefore, the BWLM proposes to combine the two balanced loss functions to better balance the accuracy of Many classes and Few classes, and then reach the highest overall accuracy. For some balanced losses, such as Focal [31], Range Loss [30], Equalization Loss [48], BS [32], etc., they often choose to focus on common or rare classes, resulting in addressing one side and ignoring the other. BWLM addresses both common and rare classes, providing a more comprehensive retention of \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **One-stage** & **Many** & **Medium** & **Few** & **All** \\ \hline CE [2] & ✓ & **65.9** & 37.5 & 7.7 & 44.4 \\ CE+CB [31] & ✓ & 39.6 & 32.7 & 16.8 & 33.2 \\ KD [65] & ✓ & 58.8 & 26.6 & 3.4 & 35.8 \\ Focal [31] & ✓ & 36.4 & 29.9 & 16.0 & 30.5 \\ OLTR [34] & ✗ & 43.2 & 35.1 & 18.5 & 35.6 \\ LFME [51] & ✗ & 47.1 & 35.0 & 17.5 & 37.2 \\ Range Loss [30] & ✓ & 35.8 & 30.3 & 17.6 & 30.7 \\ Equalization Loss [48] & ✓ & - & - & - & 36.4 \\ BS [32] & ✓ & 50.3 & 39.5 & 25.3 & 41.8 \\ NCM [2] & ✗ & 53.1 & 42.3 & 26.5 & 44.3 \\ \(\tau\)-Norm [2] & ✗ & 56.6 & 44.2 & 26.4 & 46.7 \\ cRT [2] & ✗ & 58.8 & 44.0 & 26.1 & 47.3 \\ CE+CR [64] & ✓ & **65.1** & 40.7 & 19.5 & 47.3 \\ \hline \hline \multicolumn{5}{c}{Ourmethods} \\ \hline naive & ✓ & 57.6 & 32.5 & 13.4 & 39.6 \\ Weights Transfer & & & & \\ +CE & ✗ & 55.5 & 48.5 & 19.4 & 44.6 \\ +CEW & ✗ & 62.8 & **51.6** & 13.1 & 46.4 \\ +CBW & ✗ & 61.7 & 50.2 & 26.3 & 47 \\ +BSW & ✗ & 58.2 & 50.1 & **29.7** & **47.3** \\ +BWLM & ✗ & 60.0 & **50.8** & **26.9** & **47.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy (%) on ImageNet-LT. In terms of results, our method has achieved competitive results compared to existing loss methods and two-stage transfer methods. Although not as advanced as some recent methods, these training methods added additional techniques such as Mixup and data augmentation. The best and the second-best results are shown in **underline** **bold** and **bold**, respectively. its classification ability for Many classes, in the meantime improving its classification performance for Medium and Few classes. In Table 4, this conclusion is also valid on other long-tailed distribution datasets with different IFs. On the four small CIFAR100-LT with different IFs, our model performs best at IF = 100 and IF = 200.