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We propose InCA, a lightweight method for transfer learning thatcross-attends to any activation layer of a pre-trained model. During training,InCA uses a single forward pass to extract multiple activations, which arepassed to external cross-attention adapters, trained anew and combined orselected for downstream tasks. We show that, even when selecting a singletop-scoring adapter, InCA achieves performance comparable to full fine-tuning,at a cost comparable to fine-tuning just the last layer. For example, with across-attention probe 1.3% the size of a pre-trained ViT-L/16 model, we achieveperformance within 0.2% of the full fine-tuning paragon at a computationaltraining cost of 51% of the baseline, on average across 11 downstreamclassification. Unlike other forms of efficient adaptation, InCA does notrequire backpropagating through the pre-trained model, thus leaving itsexecution unaltered at both training and inference. The versatility of InCA isbest illustrated in fine-grained tasks, which may require accessing informationabsent in the last layer but accessible in intermediate layer activations.Since the backbone is fixed, InCA allows parallel ensembling as well asparallel execution of multiple tasks. InCA achieves state-of-the-artperformance in the ImageNet-to-Sketch multi-task benchmark.

Your representations are in the network: composable and parallel adaptation for large scale models