transfer learning

7.8. transfer learning#

See also notes on đź“Ś causal inference for some close connections.

For neural-net specific transferring see đź“Ś adaption/transfer.

7.8.1. overviews#

transfer_taxonomy (from this paper)

7.8.2. domain adaptation algorithms#

Domain test bed available here, for generalizating to new domains (i.e. performing well on domains that differ from previous seen data)

  • Empirical Risk Minimization (ERM, Vapnik, 1998) - standard training

  • Invariant Risk Minimization (IRM, Arjovsky et al., 2019) - learns a feature representation such that the optimal linear classifier on top of that representation matches across domains.

  • distributional robust optimization

    • instead of minimizing training err, minimize maximum training err over different perturbations

    • Group Distributionally Robust Optimization (GroupDRO, Sagawa et al., 2020) - ERM + increase importance of domains with larger errors (see also papers from Sugiyama group e.g. 1, 2)

      • minimize error for worst group

    • Variance Risk Extrapolation (VREx, Krueger et al., 2020) - encourages robustness over affine combinations of training risks, by encouraging strict equality between training risks

  • Interdomain Mixup (Mixup, Yan et al., 2020) - ERM on linear interpolations of examples from random pairs of domains + their labels

  • Marginal Transfer Learning (MTL, Blanchard et al., 2011-2020) - augment original feature space with feature vector marginal distributions and then treat as a supervised learning problem

  • MAML (finn, abbeel, & levine, 2017) - minimize parameters for metalearning including finetuning as part of the process (intuitively, find parameters that improve performance on a task after finetuning on that task)

    • \(\min _\theta \underbrace{\mathbb{E}_\tau }_{\text{average over tasks } \tau}\left[\mathcal{L}_\tau\left(\underbrace{U_\tau(\theta)}_{\text{finetuned model}}\right)\right]\)​

      • compute finetuned models then take gradient wrt to held-out samples from the same tasks

    • Meta Learning Domain Generalization (MLDG, Li et al., 2017) - use MAML to meta-learn how to generalize across domains

  • learning more diverse predictors

    • Representation Self-Challenging (RSC, Huang et al., 2020) - adds dropout-like regularization to important features, forcing model to depend on many features

    • Spectral Decoupling (SD, Pezeshki et al., 2020) - regularization which forces model to learn more predictive features, even when only a few suffice

  • embedding prior knowledge

    • Style Agnostic Networks (SagNet, Nam et al., 2020) - penalize style features (assumed to be spurious)

    • Penalizing explanations (Rieger et al. 2020) - penalize spurious features using prior knowledge

  • Domain adaptation under structural causal models (chen & buhlmann, 2020)

    • make clearer assumptions for domain adaptation to work

    • introduce CIRM, which works better when both covariates and labels are perturbed in target data

  • kernel approach (blanchard, lee & scott, 2011) - find an appropriate RKHS and optimize a regularized empirical risk over the space

  • In-N-Out (xie…lang, 2020) - if we have many features, rather than using them all as features, can use some as features and some as targets when we shift, to learn the domain shift

  • Prospective Learning: Learning for a Dynamic Future (de silva…voelstein, chaudhari, 2024)

    • define prospective learning as setting where optimal hypothesis changes over time (like multi-task, meta-, continual, and lifelong learning)

    • introduce prospective ERM, which is shown to be consistent (unlike standard empirical risk minimization from PAC learning) - basically, incorporates time as an input in addition to the data

7.8.2.1. domain invariance#

key idea: want repr. to be invariant to domain label

7.8.2.2. feature learning#

7.8.2.3. dynamic selection#

Dynamic Selection (DS) refers to techniques in which, for a new test point, pre-trained classifiers are selected/combined from a pool at test time review paper (cruz et al. 2018), python package

  1. define region of competence

    1. clustering

    2. kNN - more refined than clustering

    3. decision space - e.g. a model’s classification boundary, internal splits in a model

    4. potential function - weight all the points (e.g. by their distance to the query point)

  2. criteria for selection

    1. individual scores: acc, prob. behavior, rank, meta-learning, complexity

    2. group: data handling, ambiguity, diversity

  3. combination

    1. non-trainable: mean, majority vote, product, median, etc.

    2. trainable: learn the combination of models

      1. related: in mixture of experts models + combination are trained jointly

    3. dynamic weighting: combine using local competence of base classifiers

    4. Oracle baseline - selects classifier predicts correct label, if such a classifier exists

7.8.2.4. test-time adaptation#

  • test-time adaptation

    • test-time augmentation

    • batch normalization (AdaBN, li…hou, 2016)

    • label shift estimation (BBSE, lipton, wang, & smola, 2018) - \(p(y)\) shifts but \(P(x|y)\) does not

    • entropy minimization (test-time entropy minimization, TENT, wang…olshausen, darrel, 2020) - optimize for model confidence (entropy of predictions), using only norm. statistics and channel-wise affine transformations

      • Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models (shu…anandkumar, xiao, 2022) - optimize prompt to minimize the entropy with confidence selection so that the model has consistent predictions across different augmented views of each test sample

    • combining train-time and test-time adaptation: Adaptive Risk Minimization (ARM, zhang et al., 2020) - combines groups at training time + batches at test-time

      • meta-train the model using simulated distribution shifts, which is enabled by the training groups, such that it exhibits strong post-adaptation performance on each shift

  • TTT: Test-Time Training with Self-Supervision for Generalization under Distribution Shifts (sun…efros, hardt. 2020) - at test-time, update parameters for self-supervised rotation prediction task then use for classification

    • TTT with masked autoencoders (gandelsman, sun, …, efros, 2022) - use reconstructed with masked autoencoder and improve performance on robustness tasks

    • Test-Time Training on Video Streams (wang, sun, …, efros, wang, 2023) - use masked autoencoder per-frame and incrementally update across each frame

    • M-TTT: Learning to (Learn at Test Time) (sun, li, dalal, …guestrin, 2024)mttt

    • analysis

      • TTT for Reading Comprehension (banerjee et al. 2021) - uses self-supervision to train models on synthetically generated question-answer pairs, and then infers answers to unseen human-authored questions for this context

      • TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? (liu et al. 2021) - explore different test-time adaptation methods and combine Test-time feature alignment with Test-time contrastive learning

  • Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors (amos, berant, & gupta, 2024) - when transferring to a specific task, should first finetune the LM on the task before doing supervised finetuning

7.8.3. adv attacks#

  • Adversarial Attacks and Defenses in Images, Graphs and Text: A Review (xu et al. 2019)

  • attacks

    • fast gradient step method - keep adding gradient to maximize noise (limit amplitude of pixel’s channel to stay imperceptible)

    • Barrage of Random Transforms for Adversarially Robust Defense (raff et al. 2019)

    • DeepFool: a simple and accurate method to fool deep neural networks (Moosavi-Dezfooli et. al 2016)

  • defenses

    • Adversarial training - training data is augmented with adv examples (Szegedy et al., 2014b; Madry et al., 2017; Tramer et al., 2017; Yu et al., 2019)

      • \[\min _{\boldsymbol{\theta}} \frac{1}{N} \sum_{n=1}^{N} \operatorname{Loss}\left(f_{\theta}\left(x_{n}\right), y_{n}\right)+\lambda\left[\max _{\|\delta\|_{\infty} \leq \epsilon} \operatorname{Loss}\left(f_{\theta}\left(x_{n}+\delta\right), y_{n}\right)\right]\]

      • this perspective differs from “robust statistics” which is usually robustness against some kind of model misspecification/assumptions, not to distr. shift

        • robust stat usually assumes a generative distr. as well

        • still often ends up with the same soln (e.g. ridge regr. corresponds to certain robusteness)

    • Stochasticity: certain inputs or hidden activations are shuffled or randomized (Xie et al., 2017; Prakash et al., 2018; Dhillon et al., 2018)

    • Preprocessing: inputs or hidden activations are quantized, projected into a different representation or are otherwise preprocessed (Guo et al., 2017; Buckman et al., 2018; Kabilan et al., 2018)

    • Manifold projections: an input sample is projected in a lower dimensional space in which the neural network has been trained to be particularly robust (Ilyas et al., 2017; Lamb et al., 2018)

    • Regularization in the loss function: an additional penalty term is added to the optimized objective function to upper bound or to approximate the adversarial loss (Hein and Andriushchenko, 2017; Yan et al., 2018)

    • constraint

      • robustness as a constraint not a loss (Constrained Learning with Non-Convex Losses (chamon et al. 2021))

        • \[\begin{split}\begin{aligned} \min _{\boldsymbol{\theta}} & \frac{1}{N} \sum_{n=1}^{N} \operatorname{Loss}\left(f_{\theta}\left(x_{n}\right), y_{n}\right) \\ \text { subject to } & \frac{1}{N} \sum_{n=1}^{N}\left[\max _{\|\delta\|_{\infty} \leq \epsilon} \operatorname{Loss}\left(f_{\theta}\left(\boldsymbol{x}_{n}+\delta\right), y_{n}\right)\right] \leq c \end{aligned}\end{split}\]

        • when penalty is convex, these 2 problems are the same

    • a possible defense against adversarial attacks is to solve the anticausal classification problem by modeling the causal generative direction, a method which in vision is referred to as analysis by synthesis (Schott et al., 2019)

  • robustness vs accuracy

  • adversarial examples

  • adversarial transferability

  • robustness

    • smoothness yields robustness (but can be robust without smoothness)

    • margin idea - data points close to the boundary are not robust

      • we want our boundary to go through regions where data is scarce

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