Dynamic against Dynamic: An Open-Set Self-Learning Framework
Authors: Haifeng Yang, Chuanxing Geng, Pong C. Yuen, Songcan Chen
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method establishes new performance milestones respectively in almost all standard and cross-data benchmarks. ... Extensive experiments verify the effectiveness of our OSSL, establishing new performance milestones respectively in almost all standard and cross-data benchmark datasets. ... Table 1: Evaluation on open-set detection (AUROC) under the standard-dataset setting. ... Table 2: Macro-F1 score (%) of different methods under the crossdataset setting (MNIST as the ID data). |
| Researcher Affiliation | Academia | Haifeng Yang1,2 , Chuanxing Geng1,2,3 , Pong C. Yuen3 and Songcan Chen1,2 1Nanjing University of Aeronautics and Astronautics 2MIIT Key Laboratory of Pattern Analysis and Machine Intelligence 3Hong Kong Baptist University |
| Pseudocode | Yes | Algorithm 1 Training Procedure of OSSL Framework |
| Open Source Code | Yes | 1https://github.com/Chuanxing Geng/OSSL |
| Open Datasets | Yes | Datastes We here follow the protocol defined in [Neal et al., 2018], and provide six standard OSR benchmarks: MNIST, SVHN, Cifar10. For MNIST [Lake et al., 2015], SVHN [Netzer et al., 2011], and CIFAR10 [Krizhevsky, 2009]... Tiny Image Net. Tiny Image Net, a derived subset of the larger Image Net [Russakovsky et al., 2014] dataset... |
| Dataset Splits | No | The paper defines training and test sets but does not specify a validation set or explicit percentages/counts for train/validation/test splits. The partitioning of the test set into three parts is for the self-learning process, not a standard validation split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) were mentioned for running experiments. Only software dependencies and general experimental setup details are provided. |
| Software Dependencies | No | The paper mentions using 'Stochastic Gradient Descent (SGD) technique as the optimizer' and a 'network architecture in [Vaze et al., 2022] as the backbone' but does not provide specific version numbers for any software libraries or environments. |
| Experiment Setup | Yes | For the threshold parameters used in the partition of test set, we set µ = 0.3, γ = 0.03 for Tiny Image Net, µ = 0.5, γ = 0.03 for Cifar10, Cifar+10, Cifar+50, while µ = 0.8, γ = 0.02 for MNIST and SVHN. In addition, the number of samples from Trs in each batch is set to 16 (the batch-size is 256), while the hyper-parameter λ in LMar is set to 2 for all benchmark datasets. ... Considering that the feature extractor F( ) is a already well-trained network, we here set its learning rate to 10 4 for Tiny Image Net while 10 5 for other datasets. Furthermore, the learning rates of other parts except for F( ) are uniformly set to 0.01. |