Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse

• URL: http://arxiv.org/abs/2207.09535v1
• Date: Tue, 19 Jul 2022 20:07:17 GMT
• Title: Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse
• Authors: Sachit Menon, David Blei, Carl Vondrick
• Abstract summary: We introduce inference critics that detect and incentivize against posterior collapse by requiring correspondence between latent variables and the observations. This approach is straightforward to implement and requires significantly less training time than prior methods.
• Score: 20.258298183228824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational autoencoders (VAEs) suffer from posterior collapse, where the powerful neural networks used for modeling and inference optimize the objective without meaningfully using the latent representation. We introduce inference critics that detect and incentivize against posterior collapse by requiring correspondence between latent variables and the observations. By connecting the critic's objective to the literature in self-supervised contrastive representation learning, we show both theoretically and empirically that optimizing inference critics increases the mutual information between observations and latents, mitigating posterior collapse. This approach is straightforward to implement and requires significantly less training time than prior methods, yet obtains competitive results on three established datasets. Overall, the approach lays the foundation to bridge the previously disconnected frameworks of contrastive learning and probabilistic modeling with variational autoencoders, underscoring the benefits both communities may find at their intersection.

Related papers

• Separating common from salient patterns with Contrastive Representation Learning [2.250968907999846]
  Contrastive Analysis aims at separating common factors of variation between two datasets. Current models based on Variational Auto-Encoders have shown poor performance in learning semantically-expressive representations. We propose to leverage the ability of Contrastive Learning to learn semantically expressive representations well adapted for Contrastive Analysis.
  arXiv  Detail & Related papers  (2024-02-19T08:17:13Z)
• RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding [6.738409533239947]
  The Recap, Deliberate, and Respond (RDR) paradigm addresses this issue by incorporating three distinct objectives within the neural network pipeline. By cascading these three models, we mitigate the potential for gaming the benchmark and establish a robust method for capturing the underlying semantic patterns. Our results demonstrate improved performance compared to competitive baselines, with an enhancement of up to 2% on standard metrics.
  arXiv  Detail & Related papers  (2023-12-15T16:41:48Z)
• Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations [6.265789210037749]
  We introduce a novel Identical and Fraternal Twins of Contrastive Learning framework, capable of simultaneously adapting to various positive pairs generated by different augmentation techniques. We also present proof-of-concept experiments combined with the contrastive objective to prove the validity of the proposed Twins Loss.
  arXiv  Detail & Related papers  (2023-07-20T15:02:42Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)
• Hybrid Predictive Coding: Inferring, Fast and Slow [62.997667081978825]
  We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner. We demonstrate that our model is inherently sensitive to its uncertainty and adaptively balances balances to obtain accurate beliefs using minimum computational expense.
  arXiv  Detail & Related papers  (2022-04-05T12:52:45Z)
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective [72.55093886515824]
  We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables. We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
  arXiv  Detail & Related papers  (2021-11-29T18:59:09Z)
• Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning [57.4036085386653]
  We show that prompt-based models for sentence pair classification tasks still suffer from a common pitfall of adopting inferences based on lexical overlap. We then show that adding a regularization that preserves pretraining weights is effective in mitigating this destructive tendency of few-shot finetuning.
  arXiv  Detail & Related papers  (2021-09-09T10:10:29Z)
• Recurrent Feedback Improves Recognition of Partially Occluded Objects [1.452875650827562]
  We investigate if and how artificial neural networks also benefit from recurrence. We find that classification accuracy is significantly higher for recurrent models when compared to feedforward models of matched parametric complexity.
  arXiv  Detail & Related papers  (2021-04-21T16:18:34Z)
• Consistency Guided Scene Flow Estimation [159.24395181068218]
  CGSF is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. We show that the proposed model can reliably predict disparity and scene flow in challenging imagery. It achieves better generalization than the state-of-the-art, and adapts quickly and robustly to unseen domains.
  arXiv  Detail & Related papers  (2020-06-19T17:28:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.