Data Translation Block for Deep Neural Network Domain Adaptation
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Solution Overview
Problem
Deep neural networks (DNNs) face performance issues when processing data from domains outside their training domain, requiring retraining or new DNNs, which can be costly and resource-intensive, especially in hardware implementations.
Innovation Solution
A data translation block (DTB) is optimized using a generator and discriminator model, where the objective function is updated to incorporate the average loss of DNNs processing transformed data, allowing DNNs to effectively handle data from new domains without the need for retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If DNNs are trained on training data from a particular domain, then the DNNs can accurately process data within that domain, but the DNNs cannot process data from outside domains or require retraining which is costly and resource-intensive
Solution Approach 1:
The system performs preliminary action by training auxiliary DNNs on source domain data before actual deployment. These pre-trained auxiliary DNNs generate predictions that are used to create pseudo-labels for unlabeled target domain data, enabling the main DNN to adapt to new domains without full retraining.
Solution Approach 2:
The system uses an intermediary mechanism where auxiliary DNNs act as mediators between the source domain training data and the target domain application. The auxiliary DNNs translate source domain knowledge into pseudo-labels that guide the adaptation of the main DNN to the target domain.
2Measurement precision
If manual labeling is performed on training data, then the accuracy of training data improves, but the time commitment and resources required increase significantly
Solution Approach 1:
The system implements self-service by using the main DNN's own predictions on unlabeled target domain data as training labels. The DNN processes its own outputs, and these predictions are refined through the auxiliary DNNs to create self-generated pseudo-labels, eliminating the need for manual human labeling.
Solution Approach 2:
The system employs feedback loops where the main DNN's predictions are fed back through auxiliary DNNs to generate improved pseudo-labels. These pseudo-labels are then used to retrain the main DNN, creating a continuous feedback cycle that progressively improves labeling accuracy without human intervention.
3Adaptability or versatility
If new training data is acquired and labeled for new domains, then the DNNs can process new domain data, but the computing resources and time required for training increase
Solution Approach 1:
The system applies partial action by using only the necessary components (auxiliary DNNs) to generate pseudo-labels for adaptation, rather than performing complete retraining of the entire DNN system. This partial approach reduces computing resource consumption while still achieving domain adaptation.
Solution Approach 2:
The system changes parameters by modifying the training objective function to incorporate pseudo-labels generated by auxiliary DNNs. Instead of using traditional supervised learning parameters, the system uses self-generated prediction parameters that require fewer computational resources than full retraining.
4Speed
If DNNs are implemented in hardware, then processing speed improves, but retraining or creating new DNNs becomes impossible
Solution Approach 1:
The system performs preliminary action by pre-training auxiliary DNNs with domain adaptation capabilities before hardware deployment. These pre-trained auxiliary models are embedded in the hardware system, enabling it to adapt to new domains through pseudo-label generation without requiring full retraining after deployment.
Solution Approach 2:
The system introduces dynamics by enabling the hardware-implemented DNN to adapt its behavior through pseudo-label-based fine-tuning. The auxiliary DNNs provide dynamic adaptation capabilities that allow the fixed hardware system to adjust to different domains through self-generated training signals.
Data Source
AI summary
The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.