Manifold Characteristics That Predict Downstream Task Performance

• URL: http://arxiv.org/abs/2205.07477v1
• Date: Mon, 16 May 2022 06:59:51 GMT
• Title: Manifold Characteristics That Predict Downstream Task Performance
• Authors: Ruan van der Merwe, Gregory Newman, Etienne Barnard
• Abstract summary: We show that differences between methods can be understood more clearly by investigating the representation manifold directly. We propose a framework and new metric to measure and compare different RMs. We show that self-supervised methods learn an RM where alterations lead to large but constant size changes, indicating a smoother RM than fully supervised methods.
• Score: 2.642698101441705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretraining methods are typically compared by evaluating the accuracy of linear classifiers, transfer learning performance, or visually inspecting the representation manifold's (RM) lower-dimensional projections. We show that the differences between methods can be understood more clearly by investigating the RM directly, which allows for a more detailed comparison. To this end, we propose a framework and new metric to measure and compare different RMs. We also investigate and report on the RM characteristics for various pretraining methods. These characteristics are measured by applying sequentially larger local alterations to the input data, using white noise injections and Projected Gradient Descent (PGD) adversarial attacks, and then tracking each datapoint. We calculate the total distance moved for each datapoint and the relative change in distance between successive alterations. We show that self-supervised methods learn an RM where alterations lead to large but constant size changes, indicating a smoother RM than fully supervised methods. We then combine these measurements into one metric, the Representation Manifold Quality Metric (RMQM), where larger values indicate larger and less variable step sizes, and show that RMQM correlates positively with performance on downstream tasks.

Related papers

• Interpreting Language Reward Models via Contrastive Explanations [14.578645682339983]
  Reward models (RMs) are a crucial component in the alignment of large language models' (LLMs) outputs with human values. We propose to use contrastive explanations to explain any binary response comparison made by an RM.
  arXiv  Detail & Related papers  (2024-11-25T15:37:27Z)
• What Representational Similarity Measures Imply about Decodable Information [6.5879381737929945]
  We show that some neural network similarity measures can be equivalently motivated from a decoding perspective. Measures like CKA and CCA quantify the average alignment between optimal linear readouts across a distribution of decoding tasks. Overall, our work demonstrates a tight link between the geometry of neural representations and the ability to linearly decode information.
  arXiv  Detail & Related papers  (2024-11-12T21:37:10Z)
• Understanding Probe Behaviors through Variational Bounds of Mutual Information [53.520525292756005]
  We provide guidelines for linear probing by constructing a novel mathematical framework leveraging information theory. First, we connect probing with the variational bounds of mutual information (MI) to relax the probe design, equating linear probing with fine-tuning. We show that the intermediate representations can have the biggest MI estimate because of the tradeoff between better separability and decreasing MI.
  arXiv  Detail & Related papers  (2023-12-15T18:38:18Z)
• Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression [53.15502562048627]
  Recent work has built the connection between self-supervised learning and the approximation of the top eigenspace of a graph Laplacian operator. This work delves into a statistical analysis of augmentation-based pretraining.
  arXiv  Detail & Related papers  (2023-06-01T15:18:55Z)
• Exogenous Data in Forecasting: FARM -- A New Measure for Relevance Evaluation [62.997667081978825]
  We introduce a new approach named FARM - Forward Relevance Aligned Metric. Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series. As a first validation step, we present the application of our FARM approach to synthetic but representative signals.
  arXiv  Detail & Related papers  (2023-04-21T15:22:33Z)
• Deep Metric Learning for Unsupervised Remote Sensing Change Detection [60.89777029184023]
  Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs) The performance of existing RS-CD methods is attributed to training on large annotated datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues.
  arXiv  Detail & Related papers  (2023-03-16T17:52:45Z)
• GULP: a prediction-based metric between representations [9.686474898346392]
  We introduce GULP, a family of distance measures between representations motivated by downstream predictive tasks. By construction, GULP provides uniform control over the difference in prediction performance between two representations. We demonstrate that GULP correctly differentiates between architecture families, converges over the course of training, and captures generalization performance on downstream linear tasks.
  arXiv  Detail & Related papers  (2022-10-12T19:17:27Z)
• Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence [2.6894568533991543]
  We introduce an auxiliary input to the neural network for the prior information about the prostate location in the MR sequence. With weakly labelled MR-TRUS prostate data, we showed registration quality comparable to the state-of-the-art deep learning-based method.
  arXiv  Detail & Related papers  (2021-02-09T16:48:59Z)
• Attentional-Biased Stochastic Gradient Descent [74.49926199036481]
  We present a provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch. ABSGD is flexible enough to combine with other robust losses without any additional cost.
  arXiv  Detail & Related papers  (2020-12-13T03:41:52Z)
• Evaluating representations by the complexity of learning low-loss predictors [55.94170724668857]
  We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learning a predictor on top of the representation that achieves low loss on a task of interest.
  arXiv  Detail & Related papers  (2020-09-15T22:06:58Z)
• Transferred Discrepancy: Quantifying the Difference Between Representations [35.957762733342804]
  Transferred discrepancy (TD) is a metric that defines the difference between two representations based on their downstream-task performance. We show how TD correlates with downstream tasks and the necessity to define metrics in such a task-dependent fashion. TD may also be used to evaluate the effectiveness of different training strategies.
  arXiv  Detail & Related papers  (2020-07-24T10:59:11Z)

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.