Disentangling Multi-view Representations via Curriculum Learning with Learnable Prior
Authors: Kai Guo, Jiedong Wang, Xi Peng, Peng Hu, Hao Wang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five real-world datasets show that the proposed model outperforms its counterparts markedly. ... We evaluate our CL2P using five real-world multi-view datasets. Extensive experimental results demonstrate the effectiveness of the proposed method and its superior performance in comparison to baselines. ... 4 Experiments 4.1 Experimental Setup Datasets. We evaluate our CL2P and other competitive methods using five real-world datasets ... Overall Evaluation. Tables 2 and 3 show the performance of clustering and classification tasks, respectively. ... Ablation Study. We conduct an ablation study to measure the contribution of the four key components |
| Researcher Affiliation | Academia | Kai Guo1 , Jiedong Wang1 , Xi Peng1,2 , Peng Hu1 , Hao Wang1 1College of Computer Science, Sichuan University, China 2National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, China |
| Pseudocode | Yes | Algorithm 1 Training of the proposed CL2P. Input: Multi-view dataset D = {x1, , xm}. Parameter: Total training epochs Tmax, current training epoch T , K pseudo-inputs of each view, parameters θc, ϕc, θs, ϕs of encoders and decoders. Output: View-consistent representations c, and view-specific representations {sv}m v=1. 1: Initial the K pseudo-inputs for each view 2: while T Tmax do 3: c Ec({xv}m v=1), and {sv}m v=1 {Ev s (xv)}m v=1 4: Compute the consistency-loss Lc using Eq. (6) 5: Compute the specificity-loss Lv s using Eq. (7) 6: Compute the disentangling-loss Lv d using Eq. (13) 7: Update T T + 1, λ 1 ( T Tmax )2 8: Compute the total loss Lmodel using Eq. (14) via λ 9: Update θc, ϕc, θs, ϕs Lmodel(θc, ϕc, θs, ϕs) 10: end while 11: return c and {sv}m v=1 |
| Open Source Code | Yes | The code is available at https://github. com/XLearning-SCU/2025-IJCAI-CL2P. |
| Open Datasets | Yes | Datasets. We evaluate our CL2P and other competitive methods using five real-world datasets, including: (1) Edge MNIST [Le Cun et al., 1998] ... (2) Edge-Fashion [Xiao et al., 2017] ... (3) Multi-COIL-20 [Nene et al., 1996b] ... (4) Multi-COIL-100 [Nene et al., 1996a] ... (5) Multi-Office-31 [Saenko et al., 2010] |
| Dataset Splits | Yes | For classification, we apply support vector classification (SVC) [Hsu, 2003] with an 80:20 train-test split ratio. |
| Hardware Specification | Yes | We implement the proposed method and other comparison methods on Py Torch 2.1.0, utilizing one NVIDIA A10 GPU (24 GB). |
| Software Dependencies | Yes | We implement the proposed method and other comparison methods on Py Torch 2.1.0, utilizing one NVIDIA A10 GPU (24 GB). |
| Experiment Setup | Yes | Both view-consistency and view-specificity dimensions are set to 20. The number of pseudo-inputs is fixed at 250, initialized with randomly selected training data. We train our model for 200 epochs using the Adam W optimizer with the learning rate of 1 10 4 and a weight decay of 1 10 4. We set a batch size of 128 for Edge-MNIST and Edge-Fashion, and 32 for Multi-COIL-20, Multi-COIL-100, and Multi-Office-31. |