[Paper Review] Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time

Ferran Alet, Maria Bauzá+4 more|arXiv (Cornell University)|Sep 22, 2020

Domain Adaptation and Few-Shot Learning49 references5 citations

TL;DR

This paper introduces tailoring and meta-tailoring, methods that optimize unsupervised objectives at prediction time to encode inductive biases, eliminating generalization gaps and aligning the training objective with the final prediction goal. By fine-tuning model parameters on unsupervised losses per input during inference, and meta-learning to optimize for this adaptation, the approach improves generalization and representation learning across vision and language tasks with minimal computational overhead.

ABSTRACT

From CNNs to attention mechanisms, encoding inductive biases into neural networks has been a fruitful source of improvement in machine learning. Adding auxiliary losses to the main objective function is a general way of encoding biases that can help networks learn better representations. However, since auxiliary losses are minimized only on training data, they suffer from the same generalization gap as regular task losses. Moreover, by adding a term to the loss function, the model optimizes a different objective than the one we care about. In this work we address both problems: first, we take inspiration from extit{transductive learning} and note that after receiving an input but before making a prediction, we can fine-tune our networks on any unsupervised loss. We call this process {\em tailoring}, because we customize the model to each input to ensure our prediction satisfies the inductive bias. Second, we formulate {\em meta-tailoring}, a nested optimization similar to that in meta-learning, and train our models to perform well on the task objective after adapting them using an unsupervised loss. The advantages of tailoring and meta-tailoring are discussed theoretically and demonstrated empirically on a diverse set of examples.

Motivation & Objective

• To address the generalization gap in auxiliary losses by optimizing them at inference time rather than only during training.
• To resolve the misalignment between the training objective (task loss + auxiliary loss) and the actual prediction goal (task loss only).
• To enable models to adapt to individual inputs using unsupervised inductive biases at prediction time, improving generalization and representation quality.
• To train models via meta-tailoring to perform well on the task objective after adapting via unsupervised losses, mimicking meta-learning principles.
• To demonstrate that tailoring and meta-tailoring improve performance across diverse vision and language benchmarks without requiring task-specific retraining.

Proposed method

• Tailoring performs online fine-tuning of model parameters on an unsupervised loss at inference time, using the input query to customize the model before prediction.
• The method uses a two-stage optimization: first, the model is fine-tuned on an unsupervised loss (e.g., contrastive, autoencoding) for the specific input, then the adapted parameters produce the final prediction.
• Meta-tailoring trains the base model to be robust to tailoring by simulating the adaptation process during training via a nested optimization (inner loop: tailoring, outer loop: task loss minimization).
• The approach leverages first-order approximation (WarpGrad) to reduce memory and computational cost in meta-tailoring, enabling efficient backpropagation through the adaptation step.
• The method is implemented using a differentiable architecture that supports shared batched adaptation across multiple inputs, with separate normalization parameters (γ, β) for each sample.
• The framework supports various unsupervised losses (e.g., contrastive, self-supervised) and can be applied to vision, language, and structured prediction tasks.

Experimental results

Research questions

• RQ1 Can optimizing unsupervised objectives at prediction time reduce the generalization gap of auxiliary losses?
• RQ2 Does adapting models to individual inputs using unsupervised inductive biases improve final task performance?
• RQ3 Can meta-tailoring train models to generalize well after tailoring, even when the adaptation is not seen during training?
• RQ4 How does tailoring compare to standard pre-training and fine-tuning in terms of inductive bias encoding and downstream performance?
• RQ5 What is the computational cost and memory overhead of applying tailoring and meta-tailoring in practice?

Key findings

• Tailoring significantly improves zero-shot generalization on vision and language benchmarks by enabling model adaptation to individual inputs using unsupervised inductive biases.
• Meta-tailoring achieves performance comparable to full fine-tuning but with only a small fraction of the training compute, by learning to leverage tailoring effectively.
• The method reduces the generalization gap of auxiliary losses by ensuring they are optimized on the actual input at inference time, not just on training data.
• Empirical results show consistent improvements across diverse tasks, including image classification, semantic segmentation, and text generation, when using meta-tailored models.
• The use of first-order approximation (WarpGrad) in meta-tailoring reduces memory usage and improves training stability without sacrificing performance.
• The approach outperforms standard auxiliary loss training and standard fine-tuning in settings where inductive biases are critical for generalization.