ReLIC, or Representation Learning via Invariant Causal Mechanisms, is a self-supervised learning objective that enforces invariant prediction of proxy targets across augmentations through an invariance regulari
Representation Learning via Invariant Causal MechanismsJovana MitrovicBrian McWilliamsJacob C WalkerLars Holger BuesingCharles BlundellInternational Conference on Learning Representations
That being said, CIGA is definitely not the ultimate solution and it intrinsically has many limitations. Nevertheless, we hope the causal analysis and the inspired solution in CIGA could serve as an initial step towards more reliable graph learning algorithms that are able to generalize various OOD...
Concepts of causal representation learning (Schlkopf et al., 2021) (Section 2.5.3) can help when defining and becoming robust to domain shifts when there are data biases (Arjovsky et al., 2019; Krueger et al., 2021). Recent work (Wang et al., 2021d) disentangles group-invariant ...
Yang, M., Liu, F., Chen, Z., Shen, X., Hao, J., & Wang, J. (2021). Causalvae: Disentangled representation learning via neural structural causal models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition(pp. 9593–9602). ...
5 Generalization Bounds and Links to Causal Inference 虽然DBC支持无需像素重建的表征学习,但它留下了一个问题,即生成的表征到底有多好。在本节中,我们将进行理论分析,以限制通过DBC学习的表征训练的价值函数的次优性。 MDP动力学与因果推断和因果图有很强的联系,因果图是有向无环图(Jonsson & Barto,2006;Sch...
Causal-IR: Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective Xin Li, Bingchen Li, Xin Jin, Cuiling Lan, Zhibo Chen University of Science and Technology of China (USTC), Microsoft Research Asia (MSRA), Eastern Institute of Technology (EIT) New!!! |...
Bengio, Y., et al.: A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912 (2019) Blanchard, G., Lee, G., Scott, C.: Generalizing from several related classification tasks to a new unlabeled sample. NeurIPS (2011) Google Scholar Carlucci,...
首先causal里的工作大部分都是假设X是手工给定的(e.g., 不是end2end从图片中提取的)。然后一般需要比较强的假设,比方说假设整个图的形式。或者知道intervention的具体作用位置和类型。然而在IRM中,1) 我们只需要收集很多环境,只要在环境中有intervention就行,而不需要知道intervention具体形式和位置. 2) 我们目标就...
(t=0)=0.1\). For intermediate threshold values\({\bar{S}}_{i}(t=0)=20\,{{{\rm{Hz}}}\), causal spike-timing induced long-term potentiation (LTP) with a nonlinear frequency dependence (Fig.6d), whereas acausal pre-after-post timings showed a characteristic crossover from long-term...