Joint Text-Image Encoder Reprogramming for OOD Robustness
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Solution Overview
Problem
Existing methods for fine-tuning joint text-image encoders face challenges in maintaining performance on out-of-distribution tasks, with alternative approaches often degrading in-domain classification accuracy while substantially reducing out-of-distribution performance due to distortions in pre-training representations.
Innovation Solution
A model reprogramming technique that concurrently reprograms both image and text encoders using residual connections, aligning representations to improve out-of-distribution generalization and detection by minimizing intrusive changes to pre-trained models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fine-tuning methods are used on joint text-image encoders, then in-domain classification accuracy is improved, but out-of-distribution performance degrades due to distortions in pre-training representations
Solution Approach 1:
The patent segments the fine-tuning process into two distinct phases: a reprogramming phase that trains a reprogrammer to transform inputs while preserving encoder representations, and a fine-tuning phase that optimizes the reprogrammer without updating encoder weights. This segmentation prevents the encoders from being distorted during fine-tuning, thereby maintaining out-of-distribution performance while achieving in-domain accuracy improvements.
Solution Approach 2:
The patent introduces a reprogrammer as an intermediary component between the input data and the encoders. The reprogrammer transforms inputs into reprogrammed inputs that are then processed by the encoders. This intermediary allows the model to adapt to in-domain tasks without directly modifying the encoders' pre-training representations, thus preserving their generalization capability for out-of-distribution tasks.
2Productivity
If encoder representations are updated during fine-tuning, then task-specific accuracy improves, but pre-training representations are distorted
Solution Approach 1:
The patent performs preliminary action by training the reprogrammer before fine-tuning the entire model. The reprogrammer is trained to transform inputs in a way that preserves the encoders' pre-training representations. This preliminary reprogramming step enables the model to achieve task-specific accuracy without requiring updates to the encoders' weights, thereby preventing loss of pre-training representations.
Solution Approach 2:
The patent changes parameters selectively: during the reprogramming phase, the reprogrammer's parameters are updated while encoder parameters remain fixed. During the fine-tuning phase, only the reprogrammer's parameters are updated, not the encoders'. This selective parameter changes approach allows the model to adapt to new tasks while preserving the encoders' pre-training representations and avoiding distortion.
3Measurement precision
If full fine-tuning is applied to joint text-image encoders, then in-domain performance improves, but out-of-distribution generalization deteriorates
Solution Approach 1:
The patent segments the fine-tuning process into two distinct phases: a reprogramming phase that trains a reprogrammer to transform inputs while preserving encoder representations, and a fine-tuning phase that optimizes the reprogrammer without updating encoder weights. This segmentation prevents the encoders from being distorted during fine-tuning, thereby maintaining out-of-distribution performance while achieving in-domain accuracy improvements.
Solution Approach 2:
The patent creates a copy of the input data through the reprogrammer, transforming original inputs into reprogrammed inputs. This copy process allows the model to learn task-specific patterns without modifying the original encoder representations. The reprogrammed inputs serve as a bridge that enables in-domain performance improvement while preserving the encoders' generalization capability for out-of-distribution tasks.
Data Source
AI summary
Techniques to fine-tune a joint text-image encoder via model reprogramming. The joint text-image encoder includes an image encoder and a text encoder, which are trained. An image and a caption describing the image are received. A reprogrammed image is generated based on the received image and using a first function. A reprogrammed caption is generated based on the received caption and using a second function. The image encoder and the text encoder are further trained using the reprogrammed image and the reprogrammed caption. One or more parameters for each of the first and second functions are backpropagated to produce, via transfer learning, the fine-tuned joint text-image encoder.